-----------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\C
> h2.log
  log type:  text
 opened on:  26 Jul 2023, 15:52:24

. 
.         ******************************
.         **** Set directory, seed *****
.         ******************************
.                 set more off 

.                 set matsize 1000
set matsize ignored.
    Matrix sizes are no longer limited by c(matsize) in modern Statas.  Matrix
    sizes are now limited by edition of Stata.  See limits for more details.

.                 global seed ="984353"

.                 set scheme plotplain

.                 cd "$dir"
C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction

.                 
.                 *****************************
.                 **** Endogenous populism ****
.                 *****************************
.                         * AKP example *
.                         use erdogan-populism,clear

.                         gen  pub = substr(publication ,1,10)

.                         tab pub

        pub |      Freq.     Percent        Cum.
------------+-----------------------------------
 Financial  |      6,490       44.85       44.85
 Guardian.c |        419        2.90       47.75
 The Guardi |      3,133       21.65       69.40
 The New Yo |      4,428       30.60      100.00
------------+-----------------------------------
      Total |     14,470      100.00

.                         egen yrpopulist= mean(proportion),by(year)

.                         egen tag = tag(year)

.                         keep if tag==1 & year<2017
(14,455 observations deleted)

.                         sort year

.                         save temp,replace
(file temp.dta not found)
file temp.dta saved

.                         use pers-use,clear

.                         qui sum polar

.                         gen polar01=(polar+abs(r(min)))/(r(max)+abs(r(min)))
(31 missing values generated)

.                         gen akp_pop = .796 if year==2002 & country=="Turkey"
(2,391 missing values generated)

.                         replace akp_pop = .796 if year==2007 & country=="Turkey"
(1 real change made)

.                         replace akp_pop = .952 if year==2011 & country=="Turkey"
(1 real change made)

.                         replace akp_pop = .962 if year==2015 & country=="Turkey"
(1 real change made)

.                         sort year 

.                         merge year using temp
(you are using old merge syntax; see [D] merge for new syntax)
variable year does not uniquely identify observations in the master data

.  
.                         twoway (line v2x_poly year if country=="Turkey",lcol(gs8)
> lpat(dash)sort ylab(.2(.2)1) ///
>                                 tit(Democratic backsliding in Turkey)xline(2003,l
> col(gs1)) xtit(Year) ///
>                                 ytit(Electoral democracy)       ///
>                                 text(.45 2000.75 "Erdogan selected" "Prime Minist
> er {&rarr}",size(vsmall))  ///
>                                 legend(lab(1 "Electoral democracy")lab(2 "Expert-
> coded AKP populism")lab(3 "AKP populist news stories") ///
>                                 order(1 2 3)size(vsmall)col(1)pos(11)ring(0))) //
> /
>                                 (scatter akp_pop year if country=="Turkey",col(gs
> 1)) ///
>                                 (line akp_pop year if country=="Turkey",sort lpat
> (line)lcol(gs1)) ///
>                                 (lpoly yrpop year if country=="Turkey",bw(2) lpat
> (line)lcol(gs1)yaxis(2) ///
>                                 ytit("Share of AKP news stories" "describing Erdo
> gan as {it:populist}",size(small)axis(2))) 
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)

.                         gr export "$dir\golden\Ch2-AKP-Populist.pdf",as(pdf)repla
> ce 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h2-AKP-Populist.pdf saved as PDF format

. 
.                         xtset cowcode year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1991 to 2020, but with gaps
         Delta: 1 unit

.                         reg yrpop l1v2x_poly if country=="Turkey",

      Source |       SS           df       MS      Number of obs   =        15
-------------+----------------------------------   F(1, 13)        =      7.19
       Model |  .001149086         1  .001149086   Prob > F        =    0.0189
    Residual |  .002078059        13  .000159851   R-squared       =    0.3561
-------------+----------------------------------   Adj R-squared   =    0.3065
       Total |  .003227144        14   .00023051   Root MSE        =    .01264

---------------------------------------------------------------------------------
     yrpopulist | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
l1v2x_polyarchy |  -.0975425    .036381    -2.68   0.019    -.1761389   -.0189461
          _cons |   .0813235   .0214815     3.79   0.002     .0349157    .1277314
---------------------------------------------------------------------------------

.                         est store akp1

.                         abar,lags(5) 
Arellano-Bond test for AR(1): z =   0.03  Pr > z = 0.9755
Arellano-Bond test for AR(2): z =   0.37  Pr > z = 0.7107
Arellano-Bond test for AR(3): z =  -1.38  Pr > z = 0.1690
Arellano-Bond test for AR(4): z =  -1.56  Pr > z = 0.1191
Arellano-Bond test for AR(5): z =   0.06  Pr > z = 0.9557

.                         reg yrpop l1v2x_poly l1.yrpop if country=="Turkey",

      Source |       SS           df       MS      Number of obs   =        14
-------------+----------------------------------   F(2, 11)        =      2.97
       Model |  .001099905         2  .000549952   Prob > F        =    0.0929
    Residual |   .00203564        11  .000185058   R-squared       =    0.3508
-------------+----------------------------------   Adj R-squared   =    0.2327
       Total |  .003135545        13  .000241196   Root MSE        =     .0136

---------------------------------------------------------------------------------
     yrpopulist | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
l1v2x_polyarchy |  -.0964532     .03966    -2.43   0.033    -.1837442   -.0091622
                |
     yrpopulist |
            L1. |   -.073361    .525015    -0.14   0.891    -1.228911    1.082189
                |
          _cons |   .0826526   .0270068     3.06   0.011     .0232112    .1420941
---------------------------------------------------------------------------------

.                         est store akp2

.                         reg d.yrpop d.l1v2x_poly if country=="Turkey",

      Source |       SS           df       MS      Number of obs   =        14
-------------+----------------------------------   F(1, 12)        =      4.12
       Model |  .000944754         1  .000944754   Prob > F        =    0.0652
    Residual |   .00275227        12  .000229356   R-squared       =    0.2555
-------------+----------------------------------   Adj R-squared   =    0.1935
       Total |  .003697024        13  .000284386   Root MSE        =    .01514

---------------------------------------------------------------------------------
   D.yrpopulist | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
l1v2x_polyarchy |
            D1. |  -.2936293   .1446755    -2.03   0.065    -.6088502    .0215916
                |
          _cons |  -.0008082   .0047255    -0.17   0.867    -.0111042    .0094878
---------------------------------------------------------------------------------

.                         est store akp3

.                         reg d.yrpop d.l1v2x_poly l1.yrpop if country=="Turkey",

      Source |       SS           df       MS      Number of obs   =        14
-------------+----------------------------------   F(2, 11)        =      3.68
       Model |  .001482371         2  .000741186   Prob > F        =    0.0597
    Residual |  .002214653        11  .000201332   R-squared       =    0.4010
-------------+----------------------------------   Adj R-squared   =    0.2920
       Total |  .003697024        13  .000284386   Root MSE        =    .01419

---------------------------------------------------------------------------------
   D.yrpopulist | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
l1v2x_polyarchy |
            D1. |  -.2891139   .1355772    -2.13   0.056    -.5875174    .0092896
                |
     yrpopulist |
            L1. |  -.8878174   .5433045    -1.63   0.131    -2.083623    .3079878
                |
          _cons |   .0178381   .0122395     1.46   0.173    -.0091009    .0447771
---------------------------------------------------------------------------------

.                         est store akp4

.                         reg d.yrpop d.l1v2x_poly l1.yrpop if country=="Turkey" & 
> year<2016

      Source |       SS           df       MS      Number of obs   =        13
-------------+----------------------------------   F(2, 10)        =     12.36
       Model |  .001104786         2  .000552393   Prob > F        =    0.0020
    Residual |   .00044676        10  .000044676   R-squared       =    0.7121
-------------+----------------------------------   Adj R-squared   =    0.6545
       Total |  .001551547        12  .000129296   Root MSE        =    .00668

---------------------------------------------------------------------------------
   D.yrpopulist | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
l1v2x_polyarchy |
            D1. |  -.1367618   .0683037    -2.00   0.073    -.2889518    .0154283
                |
     yrpopulist |
            L1. |  -1.137791   .2589984    -4.39   0.001    -1.714875   -.5607066
                |
          _cons |   .0222701   .0058085     3.83   0.003     .0093279    .0352122
---------------------------------------------------------------------------------

.                         est store akp5

.                         
.                         local var  = "v2x_polyarchy v2x_libdem v2x_partipdem"

.                         foreach v of local var {
  2.                                 qui var yrpop  `v' if country=="Turkey" , lags
> (1/1) dfk small
  3.                                 vargranger                      
  4.                         }

   Granger causality Wald tests
  +------------------------------------------------------------------------+
  |          Equation           Excluded |     F      df    df_r  Prob > F |
  |--------------------------------------+---------------------------------|
  |        yrpopulist      v2x_polyarchy |  5.9146     1      11   0.0333  |
  |        yrpopulist                ALL |  5.9146     1      11   0.0333  |
  |--------------------------------------+---------------------------------|
  |     v2x_polyarchy         yrpopulist |  .05265     1      11   0.8227  |
  |     v2x_polyarchy                ALL |  .05265     1      11   0.8227  |
  +------------------------------------------------------------------------+

   Granger causality Wald tests
  +------------------------------------------------------------------------+
  |          Equation           Excluded |     F      df    df_r  Prob > F |
  |--------------------------------------+---------------------------------|
  |        yrpopulist         v2x_libdem |  5.1813     1      11   0.0438  |
  |        yrpopulist                ALL |  5.1813     1      11   0.0438  |
  |--------------------------------------+---------------------------------|
  |        v2x_libdem         yrpopulist |   .0056     1      11   0.9417  |
  |        v2x_libdem                ALL |   .0056     1      11   0.9417  |
  +------------------------------------------------------------------------+

   Granger causality Wald tests
  +------------------------------------------------------------------------+
  |          Equation           Excluded |     F      df    df_r  Prob > F |
  |--------------------------------------+---------------------------------|
  |        yrpopulist      v2x_partipdem |  6.9083     1      11   0.0235  |
  |        yrpopulist                ALL |  6.9083     1      11   0.0235  |
  |--------------------------------------+---------------------------------|
  |     v2x_partipdem         yrpopulist |  .48836     1      11   0.4992  |
  |     v2x_partipdem                ALL |  .48836     1      11   0.4992  |
  +------------------------------------------------------------------------+

.                         erase temp.dta

. 
.                         *********************************************************
> *****************************************************************
.                         **** Change in democracy level year prior to election is 
> correlated with change in populism score for incumbent party ****
.                         *********************************************************
> *****************************************************************
.                         use pers-use,clear

.                         tsset cowcode year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1991 to 2020, but with gaps
         Delta: 1 unit

.                         gen devpop = d.populism if v2paid == l.v2paid  /* same ru
> ling party year-to-year */
(568 missing values generated)

.                         gen dev1dem  =d.l1v2x_poly
(133 missing values generated)

.                         krls devpop dev1dem if (v2xel_elecpres ==1 | v2xel_elecpa
> rl ==1)

Pointwise Derivatives                                 Number of obs =      626 
                                                      Lambda        =    107.1 
                                                      Tolerance     =     .626 
                                                      Sigma         =        1 
                                                      Eff. df       =    1.632 
                                                      R2            =  .004852 
                                                      Looloss       =    42.39

  devpop |      Avg.       SE        t    P>|t|        P25       P50       P75     
>   
---------+--------------------------------------------------------------------
 dev1dem | -.261105   .135934   -1.921    0.055   -.347626  -.342611  -.243962  
---------+--------------------------------------------------------------------


.                         * Correctly adjust for lagged outcome for ceiling effect 
> *
.                         krls devpop dev1dem l1pop if (v2xel_elecpres ==1 | v2xel_
> elecparl ==1)
Iteration =  1, Looloss: 41.33399  
Iteration =  2, Looloss: 41.07022  
Iteration =  3, Looloss: 40.84091  
Iteration =  4, Looloss: 40.67119  
Iteration =  5, Looloss: 40.5633   

Pointwise Derivatives                                    Number of obs =      626 
                                                         Lambda        =    15.18 
                                                         Tolerance     =     .626 
                                                         Sigma         =        2 
                                                         Eff. df       =    6.761 
                                                         R2            =   .06775 
                                                         Looloss       =    40.48

     devpop |      Avg.       SE        t    P>|t|        P25       P50       P75  
>      
------------+--------------------------------------------------------------------
    dev1dem | -.435512    .21943   -1.985    0.048   -.633915  -.496201  -.284377  
 l1populism | -.047875   .011458   -4.178    0.000   -.112651  -.045889   .010016  
------------+--------------------------------------------------------------------


.                         krls devpop dev1dem l1pop l2v2x_poly if (v2xel_elecpres =
> =1 | v2xel_elecparl ==1)
Iteration =  1, Looloss: 41.7087   
Iteration =  2, Looloss: 41.48422  
Iteration =  3, Looloss: 41.24778  
Iteration =  4, Looloss: 41.02796  
Iteration =  5, Looloss: 40.85099  
Iteration =  6, Looloss: 40.7337   

Pointwise Derivatives                                         Number of obs =      
> 626 
                                                              Lambda        =    17
> .24 
                                                              Tolerance     =     .
> 626 
                                                              Sigma         =      
>   3 
                                                              Eff. df       =    8.
> 663 
                                                              R2            =    .0
> 715 
                                                              Looloss       =    40
> .68

          devpop |      Avg.       SE        t    P>|t|        P25       P50       
> P75       
-----------------+-----------------------------------------------------------------
> ---
         dev1dem | -.298653   .151337   -1.973    0.049   -.409553  -.331773  -.235
> 128  
      l1populism | -.045517     .0099   -4.598    0.000   -.080671  -.048222  -.009
> 352  
 l2v2x_polyarchy | -.000501   .012684   -0.040    0.968    -.04253   .002111   .038
> 817  
-----------------+-----------------------------------------------------------------
> ---


.                         krls devpop dev1dem l1pop l2v2x_poly pres if (v2xel_elecp
> res ==1 | v2xel_elecparl ==1)
Iteration =  1, Looloss: 41.93046  
Iteration =  2, Looloss: 41.75451  
Iteration =  3, Looloss: 41.55034  
Iteration =  4, Looloss: 41.33873  
Iteration =  5, Looloss: 41.14751  
Iteration =  6, Looloss: 41.00075  

Pointwise Derivatives                                         Number of obs =      
> 626 
                                                              Lambda        =    11
> .43 
                                                              Tolerance     =     .
> 626 
                                                              Sigma         =      
>   4 
                                                              Eff. df       =    12
> .31 
                                                              R2            =   .08
> 012 
                                                              Looloss       =    40
> .88

          devpop |      Avg.       SE        t    P>|t|        P25       P50       
> P75       
-----------------+-----------------------------------------------------------------
> ---
         dev1dem | -.315509     .1473   -2.142    0.033   -.454286  -.356077  -.215
> 712  
      l1populism | -.049186   .010122   -4.859    0.000   -.079366  -.055324  -.022
> 768  
 l2v2x_polyarchy | -.000177    .01341   -0.013    0.989   -.047572   .011811   .036
> 981  
           *pres |  .004325   .006568    0.659    0.510    .002181   .005022    .00
> 645  
-----------------+-----------------------------------------------------------------
> ---
* average dy/dx is the first difference using the min and max (i.e. usually 0 to 1)

.                         krls devpop dev1dem l1pop if (v2xel_elecpres ==1 | v2xel_
> elecparl ==1) & create==1
Iteration =  1, Looloss: 12.20642  
Iteration =  2, Looloss: 12.02626  
Iteration =  3, Looloss: 11.85671  
Iteration =  4, Looloss: 11.71857  
Iteration =  5, Looloss: 11.62307  

Pointwise Derivatives                                    Number of obs =      154 
                                                         Lambda        =    3.851 
                                                         Tolerance     =     .154 
                                                         Sigma         =        2 
                                                         Eff. df       =    6.877 
                                                         R2            =     .163 
                                                         Looloss       =    11.56

     devpop |      Avg.       SE        t    P>|t|        P25       P50       P75  
>      
------------+--------------------------------------------------------------------
    dev1dem | -.760705   .371115   -2.050    0.042   -1.25145  -.837339    -.3253  
 l1populism | -.078792   .021599   -3.648    0.000   -.190516  -.093523   .018261  
------------+--------------------------------------------------------------------


.                         krls devpop dev1dem l1pop if (v2xel_elecpres ==1 | v2xel_
> elecparl ==1) & create==0
Iteration =  1, Looloss: 28.41828  

Pointwise Derivatives                                    Number of obs =      472 
                                                         Lambda        =    76.44 
                                                         Tolerance     =     .472 
                                                         Sigma         =        2 
                                                         Eff. df       =    2.523 
                                                         R2            =   .02186 
                                                         Looloss       =    28.35

     devpop |      Avg.       SE        t    P>|t|        P25       P50       P75  
>      
------------+--------------------------------------------------------------------
    dev1dem | -.141031   .084702   -1.665    0.097   -.198598  -.169918  -.125515  
 l1populism | -.020285   .007236   -2.803    0.005   -.041208  -.019979  -.002559  
------------+--------------------------------------------------------------------


. 
.  
.         ****************************
.         *** Descriptive patterns ***
.         ****************************
.                 ** Distribution **
.                 use pers-use,clear

.                 keep if year>=1991
(0 observations deleted)

.                 sfrancia persparty v2x_polyarchy

                  Shapiro–Francia W' test for normal data

    Variable |       Obs       W'          V'        z       Prob>z
-------------+-----------------------------------------------------
   persparty |     2,392    0.98135     27.581     8.031    0.00001
v2x_polyar~y |     2,392    0.90833    135.596    11.886    0.00001

.                 hist persparty, bin(25)xtit(Party personalism)ytit(Density)col(gs
> 12) ///
>                         kden kdenopts(bw(.1)lcol(blue)lpat(solid)) ///
>                         normal normopts(lcol(red)lpat(solid))  ///
>                         text(3.5 .7 "Shapiro–Francia test W'=0.981") ///
>                         text(2.9 .7 "NxT=2,392") tit("Democracies, 1991-2020")
(bin=25, start=0, width=.04)

.                 graph export "$dir\golden\T-PersParty-Distribution.pdf", as(pdf) 
>   replace
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -PersParty-Distribution.pdf saved as PDF format

.         
.                 *************************************************
.                 *** Party personalism by region and over time ***
.                 *************************************************
.                 use pers-use,clear

.                 gen d1 = gwf_duration

.                 gen d2 = gwf_duration^2

.                 gen d3 = gwf_duration^3

.                 label var persparty "Party personalism"

.                 label var e_migdppcln "GDP pc"

.                 label def region 1 "Eastern Europe and Central Asia" 2 "Latin Ame
> rica and the Caribbean"  ///
>                         3 "Middle East and North Africa" 4 "Sub-Saharan Africa" 5
>  "Western Europe and North America" ///
>                         6 "Asia and Pacific" ,replace

.                 label val pregion region

.                 reg persparty d1 d2 d3, 

      Source |       SS           df       MS      Number of obs   =     2,392
-------------+----------------------------------   F(3, 2388)      =    317.53
       Model |   34.441071         3   11.480357   Prob > F        =    0.0000
    Residual |   86.339579     2,388  .036155603   R-squared       =    0.2852
-------------+----------------------------------   Adj R-squared   =    0.2843
       Total |   120.78065     2,391  .050514701   Root MSE        =    .19015

------------------------------------------------------------------------------
   persparty | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          d1 |  -.0027586    .000797    -3.46   0.001    -.0043216   -.0011956
          d2 |  -.0000462   .0000151    -3.05   0.002    -.0000759   -.0000165
          d3 |   3.64e-07   7.63e-08     4.77   0.000     2.15e-07    5.14e-07
       _cons |    .646446   .0092243    70.08   0.000     .6283576    .6645344
------------------------------------------------------------------------------

.                 qui predict r1 if e(sample)==1,res

.                 qui sum r1

.                 qui replace r1 = r1+abs(r(min))

.                 qui sum r1

.                 qui replace r1 = r1/abs(r(max))

.                 table pregion if year==min,stat(mean persparty ivdem ld e_migdppc
> ln r1)nformat(%9.2f)

-------------------------------------------------------------------------------------------
                                   |  Party personalism   ivdem     ld   GDP pc   Residuals
-----------------------------------+-------------------------------------------------------
pregion                            |                                                       
  Eastern Europe and Central Asia  |               0.62    0.70   2.29     9.33        0.54
  Latin America and the Caribbean  |               0.54    0.70   2.80     8.89        0.50
  Middle East and North Africa     |               0.66    0.62   2.65     9.60        0.61
  Sub-Saharan Africa               |               0.62    0.58   1.85     7.74        0.53
  Western Europe and North America |               0.34    0.88   4.26    10.40        0.50
  Asia and Pacific                 |               0.53    0.61   2.55     8.83        0.49
  Total                            |               0.53    0.71   2.84     9.22        0.52
-------------------------------------------------------------------------------------------

.                 
.                 local var = "persparty ivdem gwf_duration e_migdppcln r1"

.                 foreach v of local var {
  2.                         qui sum `v'
  3.                         replace `v'  = (`v'-r(mean))/r(sd)
  4.                 }
(2,392 real changes made)
(2,392 real changes made)
(2,392 real changes made)
(2,203 real changes made)
(2,392 real changes made)

.                 sum persparty ivdem gwf_duration  e_migdppcln r1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   persparty |      2,392   -3.37e-09           1  -2.339575   2.109719
       ivdem |      2,392    9.55e-10           1  -2.866552    1.20516
gwf_duration |      2,392   -5.93e-10           1  -.9262277   3.277559
 e_migdppcln |      2,203    3.22e-16           1  -2.424545   1.888371
          r1 |      2,392    3.07e-10           1   -2.95961   2.729168

.                 egen yrcreate = mean(create),by(year)

. 
.                 twoway (lpoly create year,bw(2)lcol(gs1)xtit(Year)yaxis(2)ylab(0(
> .1).5,axis(2)) ///
>                         ytit(Share of leaders who create their own party,axis(2))
> tit(Global trends,size(large)))   ///
>                         (lpoly ivdem year,bw(2)lpat(dash)lcol(gs10)) ///
>                         (lpoly gwf_duration  year,bw(2)lpat(solid)lcol(gs10) ///
>                         ytit(Scaled indices,height(1))legend(lab(2 "Democracy lev
> el") ///
>                         lab(3 "Democracy age")lab(1 "Leader creates own party") /
> //
>                         pos(5)ring(0))saving(h1.gph,replace)ylab(-.2(.1).2)yscale
> (range(-.15 .23)))
(file h1.gph not found)
file h1.gph saved

.                 twoway (lpoly persparty year,bw(2)lpat(dash) lcol(gs1)tit(Party p
> ersonalism increasing over time,size(large)))   ///
>                         (lpoly r1 year,bw(2) lpat(solid)lcol(gs1)xtit(Year) ///
>                         ytit("")legend(lab(1 "Party personalism") ///
>                         lab(2 "Adjusted party personalism") ///
>                         pos(5)ring(0))saving(h2.gph,replace)ylab(-.2(.1).2)yscale
> (range(-.15 .23)))
(file h2.gph not found)
file h2.gph saved

.                 gr combine h1.gph h2.gph,col(2)xsize(8)

.                 erase h1.gph

.                 erase h2.gph

.                 gr export "$dir\golden\Ch2-Time-Trends.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h2-Time-Trends.pdf saved as PDF format

.         
.                 **************
.                 **** Maps ****
.                 **************
.                 use pers-use,clear

.                 keep if gwf_regime=="democracy" | gwf_regime=="provisional"
(0 observations deleted)

.                 drop tag

.                 recode persparty (0=.0001) (1=.9999)
(56 changes made to persparty)

.                 * Adjusted party personalism score *
.                         qui reg persparty ivdem ld,

.                         qui predict xb if e(sample)==1,xb

.                         qui gen r1 = persparty-xb

.                         qui sum r1

.                         qui replace r1 = r1+abs(r(min))

.                         qui sum r1

.                         qui replace r1 = r1/abs(r(max))

.                         qui reg persparty ivdem ld,

.                         qui predict r2 if e(sample)==1,res

.                         qui sum r2

.                         qui replace r2 = r2+abs(r(min))

.                         qui sum r2

.                         qui replace r2 = r2/abs(r(max))

.                 egen demcount=count(year) if year>=1991 & year<=2020,by(cowcode)

.                 gen demint=demcount/30

.                 keep if country~="" & persparty~=.
(0 observations deleted)

.                 egen avepparty =mean(persparty),by(cowcode)

.                 egen aver1=mean(r1),by(cowcode)

.                 egen tag=tag(cowcode) if avepparty~=. & demint~=.

.                 gen NAME = country

.                 replace NAME  = "Central African Rep." if country=="Central Afric
> an Republic"
(10 real changes made)

.                 replace NAME  = "Czechia" if country=="Czech Republic"
(25 real changes made)

.                 replace NAME  = "Dominican Rep." if country=="Dominican Republic"
(30 real changes made)

.                 replace NAME  = "Côte d'Ivoire" if country=="Ivory Coast"
(9 real changes made)

.                 replace NAME  = "Serbia" if country=="Federal Republic of Yugosla
> via"
(5 real changes made)

.                 replace NAME  = "Guinea-Bissau" if country=="Guinea Bissau"
(15 real changes made)

.                 replace NAME  = "Congo" if country=="Republic of Congo"
(5 real changes made)

.                 replace NAME  = "United States of America" if country=="United St
> ates"
(30 real changes made)

.                 keep if tag==1
(2,286 observations deleted)

.                 gen rdemint=round(demint,.01)

.                 replace demint=rdemint
(45 real changes made)

.                 gen ravepparty = round(avepparty,.01)

.                 replace avepparty = rave
(106 real changes made)

.                 gen raver1 = round(r1,.01)

.                 replace aver1 = raver1
(106 real changes made)

.                 keep country NAME avepparty demint aver1

.                 sort NAME

.                 merge NAME using "worlddata.dta"
(you are using old merge syntax; see [D] merge for new syntax)

.                 tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          2        1.16        1.16
          2 |         67       38.73       39.88
          3 |        104       60.12      100.00
------------+-----------------------------------
      Total |        173      100.00

.                 list country if _merge==1

     +----------------+
     |        country |
     |----------------|
 24. | Czechoslovakia |
 67. |      Mauritius |
     +----------------+

.                 drop remitgdp _merge

.                 format demint avepparty %5.2f

.                 spmap demint using worldcoor.dta if SOVEREIGNT!="Antarctica", ///
>                         id(id)ndf(gs12) ndocolor(gs1)clmethod(kmeans)clnum(5)fcol
> or(Greys)  ///
>                         title("Share of years coded as democracy",size(*0.8)) ///
>                         subtitle("1991-2020", size(*0.8))  ///
>                         legstyle(3) legend(ring(0) position(8)) ///
>                         graphregion(icolor(white)margin(tiny)) ///
>                         plotregion(icolor(white)margin(tiny))  

.                 gr export "$dir\golden\Ch1-MapDem.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h1-MapDem.pdf saved as PDF format

.                 spmap avepparty using worldcoor.dta if SOVEREIGNT!="Antarctica", 
> ///
>                         id(id)ndf(gs15)clmethod(kmeans)clnum(5)fcolor(Oranges)  /
> //
>                         title("Average level of party personalism",size(*0.8)) //
> /
>                         legstyle(3) legend(ring(0)position(8))saving(h1.gph,repla
> ce) ///
>                         graphregion(icolor(white)margin(tiny)) ///
>                         plotregion(icolor(white)margin(tiny))  
(file h1.gph not found)
file h1.gph saved

.                 spmap aver1 using worldcoor.dta if SOVEREIGNT!="Antarctica", ///
>                         id(id)ndf(gs15)clmethod(kmeans)clnum(5)fcolor(Oranges)  /
> //
>                         title("Adjusted level of party personalism",size(*0.8)) /
> //
>                         legstyle(3) legend(ring(0)position(8))saving(h2.gph,repla
> ce) ///
>                         graphregion(icolor(white)margin(tiny)) ///
>                         plotregion(icolor(white)margin(tiny)) 
(file h2.gph not found)
file h2.gph saved

.                 gr combine h1.gph h2.gph,col(1) xsize(4) ysize(4)

.                 gr export "$dir\golden\T-MapParties.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -MapParties.pdf saved as PDF format

.                 global d = "persparty"

.  
.                 ***************************************************
.                 **** Personalist parties and economic ideology ****
.                 ***************************************************
.                                 use pers-use,clear

.                                 global x = "ld ivdem pres mixed proportional"

.                                 gen s = year==min | v2xel_elecpres==1|  v2xel_ele
> cparl==1

.                                 gen proportional= v2elparlel==1 | v2elparlel==3

.                                 gen mixed =  v2elparlel==2

.                         *** Personalist parties and left-right ideology ***
.                                 desc   v2pawelf v2pariglef

Variable      Storage   Display    Value
    name         type    format    label      Variable label
-----------------------------------------------------------------------------------
v2pawelf        double  %9.0g                 Welfare
v2pariglef      double  %9.0g                 Economic left-right scale

.                                 qui sum v2pariglef

.                                 replace v2pariglef= (v2pariglef+abs(r(min)))
(2,243 real changes made)

.                                 qui sum v2pariglef

.                                 replace v2pariglef=v2pariglef/r(max)    
(2,241 real changes made)

.                                 twoway (hist persparty,col(gs12)yscale(range(0 12
> )axis(2))yaxis(2)bin(50) ///
>                                         ylab(0 "",axis(2)nolabels noticks) ytitle
> ("",axis(2)) yscale(range(0 100)axis(2))) ///
>                                         (lpolyci v2pariglef persparty,bw(.1)ytitl
> e("{&larr} Left                          Right {&rarr}") ///
>                                         yscale(alt)yscale(range(.4 .7))ylab(.4(0.
> 1)0.7, axis(1)) ///
>                                         xtit(Party personalism)legend(off)tit(Lef
> t-right economic ideology,size(large)) saving(h3.gph,replace) ///
>                                         yline(.5824661,lcol(blue*.5)) text(.57 .3
>  "Ideology mean",size(vsmall))  )      
(file h3.gph not found)
file h3.gph saved

.                                 ttest v2pariglef,by(create)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   1,636    .5865511    .0041666    .1685271    .5783788    .5947235
       1 |     607    .5612023    .0073286    .1805579    .5468097    .5755948
---------+--------------------------------------------------------------------
Combined |   2,243    .5796912    .0036358     .172194    .5725613    .5868212
---------+--------------------------------------------------------------------
    diff |            .0253489    .0081679                .0093314    .0413664
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   3.1035
H0: diff = 0                                     Degrees of freedom =     2241

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9990         Pr(|T| > |t|) = 0.0019          Pr(T > t) = 0.0010

.                                 
.                         *** Personalist parties and welfare statism ideology ***
.                                 desc v2pawelf

Variable      Storage   Display    Value
    name         type    format    label      Variable label
-----------------------------------------------------------------------------------
v2pawelf        double  %9.0g                 Welfare

.                                 qui sum v2pawelf

.                                 replace v2pawelf= (v2pawelf+abs(r(min)))
(2,243 real changes made)

.                                 qui sum v2pawelf

.                                 replace v2pawelf=v2pawelf/r(max)        
(2,241 real changes made)

.                                 twoway (hist persparty,col(gs12)yscale(range(0 12
> )axis(2))yaxis(2)bin(50) ///
>                                         ylab(0 "",axis(2)nolabels noticks) ytitle
> ("",axis(2)) yscale(range(0 100)axis(2))) ///
>                                         (lpolyci v2pawelf persparty,bw(.1)ytitle(
> "{&larr} Less welfare statist          More welfare statist {&rarr}") ///
>                                         yscale(alt)yscale(range(.4 .7))ylab(.4(0.
> 1)0.7, axis(1)) ///
>                                         xtit(Party personalism)legend(off)tit(Wel
> fare state ideology,size(large)) saving(h4.gph,replace) ///
>                                         yline(.578,lcol(blue*.5)) text(.565 .02 "
> Welfare" "statist" "mean",size(vsmall))  )     
(file h4.gph not found)
file h4.gph saved

.                                 ttest v2pawelf,by(create)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   1,636    .5769057     .004545    .1838343     .567991    .5858203
       1 |     607    .5757007    .0067868     .167209    .5623722    .5890292
---------+--------------------------------------------------------------------
Combined |   2,243    .5765796    .0037891    .1794513    .5691491      .58401
---------+--------------------------------------------------------------------
    diff |             .001205    .0085304               -.0155233    .0179333
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.1413
H0: diff = 0                                     Degrees of freedom =     2241

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5562         Pr(|T| > |t|) = 0.8877          Pr(T > t) = 0.4438

.  
.                                 global x = "ld ivdem pres mixed proportional"

.                                 reghdfe v2pariglef persparty $x if s==1,a(period 
> pregion)cluster(lid)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,122
Absorbing 2 HDFE groups                           F(   6,    543) =       1.52
Statistics robust to heteroskedasticity           Prob > F        =     0.1692
                                                  R-squared       =     0.0565
                                                  Adj R-squared   =     0.0428
                                                  Within R-sq.    =     0.0183
Number of clusters (lid)     =        544         Root MSE        =     0.1667

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
  v2pariglef | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -.0022489   .0458035    -0.05   0.961    -.0922225    .0877248
          ld |   .0253442   .0121055     2.09   0.037     .0015649    .0491234
       ivdem |  -.1728125   .0664824    -2.60   0.010    -.3034068   -.0422182
        pres |   .0080924   .0210176     0.39   0.700    -.0331934    .0493782
       mixed |   .0143354   .0240987     0.59   0.552    -.0330028    .0616736
proportional |   .0019539   .0231089     0.08   0.933    -.0434399    .0473477
       _cons |   .6194867   .0544176    11.38   0.000     .5125918    .7263815
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      period |         6           0           6     |
     pregion |         6           1           5     |
-----------------------------------------------------+

.                                 reghdfe v2pawelf   persparty $x if s==1,a(period 
> pregion)cluster(lid)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      1,122
Absorbing 2 HDFE groups                           F(   6,    543) =       2.35
Statistics robust to heteroskedasticity           Prob > F        =     0.0298
                                                  R-squared       =     0.2391
                                                  Adj R-squared   =     0.2281
                                                  Within R-sq.    =     0.0228
Number of clusters (lid)     =        544         Root MSE        =     0.1595

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2pawelf | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -.0043789   .0425467    -0.10   0.918    -.0879552    .0791974
          ld |  -.0214757   .0110455    -1.94   0.052    -.0431728    .0002214
       ivdem |     .07088   .0642084     1.10   0.270    -.0552473    .1970073
        pres |   .0034795   .0208442     0.17   0.867    -.0374656    .0444246
       mixed |   .0123183   .0228866     0.54   0.591    -.0326388    .0572753
proportional |   .0460203   .0219599     2.10   0.037     .0028836    .0891571
       _cons |   .5599379   .0493595    11.34   0.000     .4629789    .6568969
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      period |         6           0           6     |
     pregion |         6           1           5     |
-----------------------------------------------------+

. 
.                         gr combine h3.gph h4.gph,col(3) xsize(8) ysize(4) iscale(
> .8)

.                         gr export "$dir\golden\Ch2-Persparty-Ideology.pdf",as(pdf
> )replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h2-Persparty-Ideology.pdf saved as PDF format

.                         
.                         qui sum v2pariglef if s==1

.                         qui gen extreme = v2pariglef<r(mean)-r(sd) |  v2pariglef>
> r(mean)+r(sd) if v2pariglef~=.

.                         qui sum v2pariglef if s==1

.                         qui gen extreme2 = v2pariglef<r(mean)-(1.5*r(sd)) |  v2pa
> riglef>r(mean)+(1.5*r(sd)) if v2pariglef~=.

.                         qui sum v2pariglef if s==1

.                         qui gen extreme3 = abs((v2pariglef-r(mean))/r(sd)) if v2p
> ariglef~=.

.                         tab extreme extreme2 if s==1

           |       extreme2
   extreme |         0          1 |     Total
-----------+----------------------+----------
         0 |       689          0 |       689 
         1 |       326        107 |       433 
-----------+----------------------+----------
     Total |     1,015        107 |     1,122 

.                         reghdfe extreme i.period persparty $x if s==1,a(cowcode)c
> luster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 1 HDFE group                            F(  11,    539) =       2.11
Statistics robust to heteroskedasticity           Prob > F        =     0.0182
                                                  R-squared       =     0.3939
                                                  Adj R-squared   =     0.3297
                                                  Within R-sq.    =     0.0341
Number of clusters (lid)     =        540         Root MSE        =     0.3983

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
     extreme | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      period |
          2  |   .0386309   .0429873     0.90   0.369    -.0458122    .1230741
          3  |   .1778027   .0580353     3.06   0.002     .0637996    .2918057
          4  |   .1958137   .0588794     3.33   0.001     .0801524     .311475
          5  |   .1540929   .0683475     2.25   0.025     .0198329    .2883529
          6  |   .1177886   .0741473     1.59   0.113    -.0278645    .2634416
             |
   persparty |   .1742784   .1246898     1.40   0.163    -.0706591    .4192159
          ld |  -.0073135   .0389143    -0.19   0.851    -.0837557    .0691288
       ivdem |   -.647782   .3151577    -2.06   0.040     -1.26687   -.0286942
        pres |   .3213225   .1942553     1.65   0.099    -.0602677    .7029127
       mixed |   .0008541   .0882233     0.01   0.992    -.1724496    .1741577
proportional |  -.0038288   .0963695    -0.04   0.968    -.1931345     .185477
       _cons |   .5192951   .2447704     2.12   0.034     .0384743    1.000116
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
-----------------------------------------------------+

.                         reghdfe extreme2 i.period persparty $x if s==1,a(cowcode)
> cluster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 1 HDFE group                            F(  11,    539) =       0.91
Statistics robust to heteroskedasticity           Prob > F        =     0.5322
                                                  R-squared       =     0.3181
                                                  Adj R-squared   =     0.2458
                                                  Within R-sq.    =     0.0204
Number of clusters (lid)     =        540         Root MSE        =     0.2545

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    extreme2 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      period |
          2  |   .0112543   .0274346     0.41   0.682    -.0426374    .0651461
          3  |   .0455643   .0350733     1.30   0.194    -.0233329    .1144615
          4  |   .0794254   .0400044     1.99   0.048     .0008417    .1580091
          5  |   .0924723   .0455489     2.03   0.043     .0029971    .1819475
          6  |   .0804222   .0448886     1.79   0.074    -.0077557    .1686002
             |
   persparty |   .0241204   .0794079     0.30   0.761    -.1318666    .1801073
          ld |   -.026799   .0264764    -1.01   0.312    -.0788085    .0252106
       ivdem |  -.5141598   .2412574    -2.13   0.034    -.9880797   -.0402398
        pres |   .0296345   .0230905     1.28   0.200    -.0157239    .0749929
       mixed |  -.0292541   .0754948    -0.39   0.699    -.1775541     .119046
proportional |  -.0507195   .0796585    -0.64   0.525    -.2071987    .1057598
       _cons |   .5063965   .1764999     2.87   0.004     .1596845    .8531084
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
-----------------------------------------------------+

.                         reghdfe extreme3 i.period persparty $x if s==1,a(cowcode)
> cluster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 1 HDFE group                            F(  11,    539) =       1.78
Statistics robust to heteroskedasticity           Prob > F        =     0.0544
                                                  R-squared       =     0.3897
                                                  Adj R-squared   =     0.3250
                                                  Within R-sq.    =     0.0346
Number of clusters (lid)     =        540         Root MSE        =     0.4114

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    extreme3 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      period |
          2  |   .0328956   .0455526     0.72   0.471    -.0565868    .1223781
          3  |   .1238687   .0627813     1.97   0.049     .0005427    .2471947
          4  |   .2041505   .0665158     3.07   0.002     .0734885    .3348125
          5  |   .1794432   .0730773     2.46   0.014     .0358919    .3229945
          6  |   .1068766   .0777455     1.37   0.170    -.0458447     .259598
             |
   persparty |   .1735267   .1432724     1.21   0.226    -.1079141    .4549675
          ld |  -.0103851   .0421609    -0.25   0.806    -.0932048    .0724346
       ivdem |  -.6175602   .3331767    -1.85   0.064    -1.272044    .0369237
        pres |   .3690464   .2362767     1.56   0.119    -.0950896    .8331823
       mixed |   .0155717    .099968     0.16   0.876     -.180803    .2119463
proportional |  -.0256759   .1106774    -0.23   0.817    -.2430877     .191736
       _cons |   .9791268   .2606318     3.76   0.000     .4671483    1.491105
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
-----------------------------------------------------+

.                         gen lnextreme3 = ln(1+extreme3)
(149 missing values generated)

.                         reghdfe lnextreme3 i.period persparty $x if s==1,a(cowcod
> e)cluster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 1 HDFE group                            F(  11,    539) =       1.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0667
                                                  R-squared       =     0.3742
                                                  Adj R-squared   =     0.3079
                                                  Within R-sq.    =     0.0317
Number of clusters (lid)     =        540         Root MSE        =     0.2275

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
  lnextreme3 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      period |
          2  |    .017844   .0257727     0.69   0.489    -.0327833    .0684713
          3  |   .0605472   .0345963     1.75   0.081    -.0074129    .1285072
          4  |   .1054816   .0366305     2.88   0.004     .0335256    .1774376
          5  |   .0930303   .0404964     2.30   0.022     .0134803    .1725803
          6  |   .0497141   .0438775     1.13   0.258    -.0364777    .1359059
             |
   persparty |   .0898636   .0781593     1.15   0.251    -.0636706    .2433978
          ld |  -.0026857   .0239232    -0.11   0.911    -.0496798    .0443085
       ivdem |  -.2930751   .1800014    -1.63   0.104    -.6466654    .0605151
        pres |   .2089711   .1434955     1.46   0.146    -.0729079    .4908501
       mixed |   .0149937   .0532532     0.28   0.778    -.0896156     .119603
proportional |  -.0057884   .0605552    -0.10   0.924    -.1247415    .1131646
       _cons |    .605182   .1452519     4.17   0.000     .3198528    .8905113
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
-----------------------------------------------------+

.                         
.                         * Populism is associated with ideological extremism but n
> ot personalist parties *
.                         probit extreme persparty $x if s==1,cluster(lid)

Iteration 0:  Log pseudolikelihood = -748.24732  
Iteration 1:  Log pseudolikelihood = -739.13155  
Iteration 2:  Log pseudolikelihood = -739.12675  
Iteration 3:  Log pseudolikelihood = -739.12675  

Probit regression                                       Number of obs =  1,122
                                                        Wald chi2(6)  =   8.60
                                                        Prob > chi2   = 0.1975
Log pseudolikelihood = -739.12675                       Pseudo R2     = 0.0122

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
     extreme | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |   .0003564   .3028007     0.00   0.999     -.593122    .5938349
          ld |   .0670567   .0727156     0.92   0.356    -.0754632    .2095766
       ivdem |  -.5521012   .4847175    -1.14   0.255     -1.50213    .3979277
        pres |   .1590386   .1235556     1.29   0.198    -.0831259    .4012031
       mixed |   .4090823     .17959     2.28   0.023     .0570925    .7610722
proportional |   .3295141   .1623875     2.03   0.042     .0112404    .6477878
       _cons |  -.4547277   .3636398    -1.25   0.211    -1.167449    .2579933
------------------------------------------------------------------------------

.                         probit extreme populism $x if s==1,cluster(lid)

Iteration 0:  Log pseudolikelihood = -748.24732  
Iteration 1:  Log pseudolikelihood = -729.80497  
Iteration 2:  Log pseudolikelihood = -729.79589  
Iteration 3:  Log pseudolikelihood = -729.79589  

Probit regression                                       Number of obs =  1,122
                                                        Wald chi2(6)  =  16.79
                                                        Prob > chi2   = 0.0101
Log pseudolikelihood = -729.79589                       Pseudo R2     = 0.0247

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
     extreme | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
    populism |   .7622864    .268705     2.84   0.005     .2356343    1.288938
          ld |   .0841796   .0702573     1.20   0.231    -.0535222    .2218813
       ivdem |  -.5487935   .4906782    -1.12   0.263    -1.510505    .4129181
        pres |   .0768988   .1271422     0.60   0.545    -.1722953    .3260929
       mixed |   .4095076   .1788734     2.29   0.022     .0589221    .7600931
proportional |   .3712269   .1642956     2.26   0.024     .0492134    .6932403
       _cons |  -.7576023   .3140566    -2.41   0.016    -1.373142   -.1420628
------------------------------------------------------------------------------

.                         margins,dydx(populism)

Average marginal effects                                 Number of obs = 1,122
Model VCE: Robust

Expression: Pr(extreme), predict()
dy/dx wrt:  populism

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
    populism |   .2838396   .0972949     2.92   0.004     .0931451    .4745342
------------------------------------------------------------------------------

.                         probit extreme persparty populism $x if s==1,cluster(lid)

Iteration 0:  Log pseudolikelihood = -748.24732  
Iteration 1:  Log pseudolikelihood = -729.55147  
Iteration 2:  Log pseudolikelihood = -729.54251  
Iteration 3:  Log pseudolikelihood = -729.54251  

Probit regression                                       Number of obs =  1,122
                                                        Wald chi2(7)  =  16.97
                                                        Prob > chi2   = 0.0176
Log pseudolikelihood = -729.54251                       Pseudo R2     = 0.0250

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
     extreme | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -.1466439   .3060077    -0.48   0.632     -.746408    .4531201
    populism |   .7830975   .2729068     2.87   0.004       .24821    1.317985
          ld |   .0730856   .0733554     1.00   0.319    -.0706883    .2168595
       ivdem |  -.5619929   .4920113    -1.14   0.253    -1.526317    .4023316
        pres |   .0810029   .1270574     0.64   0.524     -.168025    .3300309
       mixed |   .4172396   .1803664     2.31   0.021      .063728    .7707513
proportional |   .3778711   .1658873     2.28   0.023      .052738    .7030043
       _cons |  -.6542867   .3720315    -1.76   0.079    -1.383455    .0748816
------------------------------------------------------------------------------

.                         margins,dydx(populism persparty)

Average marginal effects                                 Number of obs = 1,122
Model VCE: Robust

Expression: Pr(extreme), predict()
dy/dx wrt:  persparty populism

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |   -.054585   .1138106    -0.48   0.632    -.2776496    .1684797
    populism |   .2914907   .0987095     2.95   0.003     .0980236    .4849578
------------------------------------------------------------------------------

.                 
.                         *** Personalism less likely to be workingclass ***
.                         gen workingclass = v2pagroup_8==1 | v2pagroup_10==1 if v2
> paid~=.
(55 missing values generated)

.                         qui centile v2xpa_popul if s==1,centile(50)

.                         local c = r(c_1)

.                         gen hipop = v2xpa_popul>`c' if v2xpa_popul~=.
(149 missing values generated)

.                         qui centile persparty if s==1,centile(50)

.                         local c = r(c_1)

.                         gen hipers = persparty>`c' if persparty~=.

.                         tab hipers hipop 

           |         hipop
    hipers |         0          1 |     Total
-----------+----------------------+----------
         0 |       718        521 |     1,239 
         1 |       410        594 |     1,004 
-----------+----------------------+----------
     Total |     1,128      1,115 |     2,243 

.                         gen e = .
(2,392 missing values generated)

.                         gen hi = . 
(2,392 missing values generated)

.                         gen lo = .
(2,392 missing values generated)

.                         gen n = _n

.                         ttest workingclass,by(hipers)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   1,277    .1370399    .0096271    .3440242    .1181533    .1559265
       1 |   1,060    .0830189    .0084785     .276041    .0663822    .0996555
---------+--------------------------------------------------------------------
Combined |   2,337    .1125374    .0065386    .3160942    .0997153    .1253596
---------+--------------------------------------------------------------------
    diff |            .0540211    .0130892                .0283535    .0796887
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   4.1272
H0: diff = 0                                     Degrees of freedom =     2335

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

.                         local  m1=r(mu_1) 

.                         local se1 = r(sd_1)/(sqrt(r(N_1)))

.                         local  m2=r(mu_2)

.                         local se2 = r(sd_2)/(sqrt(r(N_2)))

.                         replace e=`m1' if _n==1
(1 real change made)

.                         replace hi = `m1' + 1.95* `se1' if _n==1
(1 real change made)

.                         replace lo = `m1' - 1.95* `se1'  if _n==1
(1 real change made)

.                         replace e=`m2' if _n==2
(1 real change made)

.                         replace hi = `m2' + 1.95*`se2' if _n==2
(1 real change made)

.                         replace lo = `m2' - 1.95*`se2' if _n==2 
(1 real change made)

.                         ttest workingclass,by(hipop)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   1,128    .0602837    .0070898    .2381173    .0463729    .0741945
       1 |   1,115    .1748879    .0113813    .3800416    .1525566    .1972192
---------+--------------------------------------------------------------------
Combined |   2,243    .1172537    .0067946    .3217941    .1039293     .130578
---------+--------------------------------------------------------------------
    diff |           -.1146042    .0133751               -.1408331   -.0883753
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -8.5685
H0: diff = 0                                     Degrees of freedom =     2241

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

.                         local  m1=r(mu_1) 

.                         local se1 = r(sd_1)/(sqrt(r(N_1)))

.                         local  m2=r(mu_2)

.                         local se2 = r(sd_2)/(sqrt(r(N_2)))

.                         replace e=`m1' if _n==4
(1 real change made)

.                         replace hi = `m1' + 1.95* `se1' if _n==4
(1 real change made)

.                         replace lo = `m1' - 1.95* `se1'  if _n==4
(1 real change made)

.                         replace e=`m2' if _n==5
(1 real change made)

.                         replace hi = `m2' + 1.95*`se2' if _n==5
(1 real change made)

.                         replace lo = `m2' - 1.95*`se2' if _n==5 
(1 real change made)

.                         twoway (bar e n if n<=5,barwidth(.5)bcol(gs13)ytit("Share
>  of parties with {it:working-class} support")) ///
>                                 (rspike hi lo n if n<=5,ylab(0(.05).2)col(gs1)leg
> end(off)xtit("")tit(Working-class party support) ///
>                                 xlab(1 `""{bf:Low} party" "personalism""' 2 `""{b
> f:High} party" "personalism""' ///
>                                 4 `""{bf:Low} party" "populism""' 5  `""{bf:High}
>  party" "populism""')xscale(range(0.5 5.5)))

.                         probit workingclass ld persparty if s==1,cluster(lid)

Iteration 0:  Log pseudolikelihood = -407.44407  
Iteration 1:  Log pseudolikelihood = -380.33488  
Iteration 2:  Log pseudolikelihood = -379.88704  
Iteration 3:  Log pseudolikelihood = -379.88656  
Iteration 4:  Log pseudolikelihood = -379.88656  

Probit regression                                       Number of obs =  1,182
                                                        Wald chi2(2)  =  17.46
                                                        Prob > chi2   = 0.0002
Log pseudolikelihood = -379.88656                       Pseudo R2     = 0.0676

                                  (Std. err. adjusted for 571 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .2655928   .0811029     3.27   0.001     .1066341    .4245516
   persparty |  -.5863151   .3679133    -1.59   0.111    -1.307412    .1347817
       _cons |  -1.811881   .3570613    -5.07   0.000    -2.511709   -1.112054
------------------------------------------------------------------------------

.                         probit workingclass ld populism if s==1,cluster(lid)

Iteration 0:  Log pseudolikelihood = -400.31664  
Iteration 1:  Log pseudolikelihood =  -355.7362  
Iteration 2:  Log pseudolikelihood = -354.20297  
Iteration 3:  Log pseudolikelihood =  -354.2005  
Iteration 4:  Log pseudolikelihood =  -354.2005  

Probit regression                                       Number of obs =  1,122
                                                        Wald chi2(2)  =  29.30
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -354.2005                        Pseudo R2     = 0.1152

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .4154209   .0924825     4.49   0.000     .2341585    .5966833
    populism |   1.463606   .3489796     4.19   0.000     .7796189    2.147594
       _cons |  -3.115946   .3760597    -8.29   0.000     -3.85301   -2.378883
------------------------------------------------------------------------------

.                         probit workingclass ld persparty populism if s==1,cluster
> (lid)

Iteration 0:  Log pseudolikelihood = -400.31664  
Iteration 1:  Log pseudolikelihood = -349.66211  
Iteration 2:  Log pseudolikelihood = -348.00187  
Iteration 3:  Log pseudolikelihood =  -347.9979  
Iteration 4:  Log pseudolikelihood =  -347.9979  

Probit regression                                       Number of obs =  1,122
                                                        Wald chi2(3)  =  29.06
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -347.9979                        Pseudo R2     = 0.1307

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .3238463   .0898289     3.61   0.000     .1477848    .4999078
   persparty |  -.9927882   .4127512    -2.41   0.016    -1.801766   -.1838107
    populism |   1.656862   .3748393     4.42   0.000     .9221901    2.391533
       _cons |  -2.415842   .3972995    -6.08   0.000    -3.194535   -1.637149
------------------------------------------------------------------------------

.                                 * pattern remains in FE model *
.                         reghdfe workingclass ld persparty if s==1,a(cowcode year)
> cluster(lid)
(dropped 5 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      1,177
Absorbing 2 HDFE groups                           F(   2,    565) =       2.02
Statistics robust to heteroskedasticity           Prob > F        =     0.1340
                                                  R-squared       =     0.3555
                                                  Adj R-squared   =     0.2761
                                                  Within R-sq.    =     0.0077
Number of clusters (lid)     =        566         Root MSE        =     0.2650

                                  (Std. err. adjusted for 566 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0565034   .0287306     1.97   0.050     .0000716    .1129352
   persparty |   .0770648   .0884217     0.87   0.384    -.0966105    .2507402
       _cons |  -.1018588   .1061249    -0.96   0.338    -.3103062    .1065887
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        99           0          99     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         reghdfe workingclass ld populism if s==1,a(cowcode year)c
> luster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 2 HDFE groups                           F(   2,    539) =      16.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4005
                                                  Adj R-squared   =     0.3236
                                                  Within R-sq.    =     0.0753
Number of clusters (lid)     =        540         Root MSE        =     0.2620

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0605996   .0291122     2.08   0.038     .0034123     .117787
    populism |   .4190259   .0749936     5.59   0.000     .2717102    .5663415
       _cons |  -.2133221   .0945778    -2.26   0.025    -.3991084   -.0275358
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         reghdfe workingclass ld persparty populism if s==1,a(cowc
> ode year)cluster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 2 HDFE groups                           F(   3,    539) =      11.09
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4007
                                                  Adj R-squared   =     0.3231
                                                  Within R-sq.    =     0.0755
Number of clusters (lid)     =        540         Root MSE        =     0.2621

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |    .059663   .0297072     2.01   0.045      .001307    .1180191
   persparty |  -.0294579   .0946584    -0.31   0.756    -.2154025    .1564867
    populism |   .4243729   .0765081     5.55   0.000     .2740823    .5746635
       _cons |  -.1972814   .1129613    -1.75   0.081    -.4191796    .0246169
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         
.                         *** Personalist parties less likely to mobilize on econom
> ic issues ***
.                         glm v2pasalie_10 ivdem ld create if s==1,fam(bin)link(pro
> bit)cluster(lid)
note: v2pasalie_10 has noninteger values

Iteration 0:  Log pseudolikelihood = -593.30658  
Iteration 1:  Log pseudolikelihood =  -593.2311  
Iteration 2:  Log pseudolikelihood =  -593.2311  

Generalized linear models                         Number of obs   =      1,122
Optimization     : ML                             Residual df     =      1,118
                                                  Scale parameter =          1
Deviance         =  571.2010103                   (1/df) Deviance =   .5109132
Pearson          =   450.816669                   (1/df) Pearson  =   .4032349

Variance function: V(u) = u*(1-u/1)               [Binomial]
Link function    : g(u) = invnorm(u)              [Probit]

                                                  AIC             =   1.064583
Log pseudolikelihood = -593.2310967               BIC             =  -7280.366

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
v2pasalie_10 | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       ivdem |   .6817992   .2521028     2.70   0.007     .1876867    1.175912
          ld |   .0612982   .0394226     1.55   0.120    -.0159688    .1385651
      create |  -.1699961   .0743664    -2.29   0.022    -.3157515   -.0242407
       _cons |  -.4296418   .1548846    -2.77   0.006      -.73321   -.1260736
------------------------------------------------------------------------------

.                         reghdfe v2pasalie_10 ivdem ld persparty if s==1,a(cowcode
>  year)cluster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 2 HDFE groups                           F(   3,    539) =       3.96
Statistics robust to heteroskedasticity           Prob > F        =     0.0082
                                                  R-squared       =     0.5738
                                                  Adj R-squared   =     0.5187
                                                  Within R-sq.    =     0.0120
Number of clusters (lid)     =        540         Root MSE        =     0.2198

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
v2pasalie_10 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       ivdem |   .3175118   .1325237     2.40   0.017     .0571855    .5778381
          ld |  -.0025131   .0222472    -0.11   0.910     -.046215    .0411888
   persparty |  -.1232316   .0597956    -2.06   0.040    -.2406925   -.0057706
       _cons |   .4200961   .1117537     3.76   0.000     .2005698    .6396223
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         reghdfe v2pasalie_10 ivdem ld populism if s==1,a(cowcode 
> year)cluster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 2 HDFE groups                           F(   3,    539) =      38.61
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6289
                                                  Adj R-squared   =     0.5809
                                                  Within R-sq.    =     0.1397
Number of clusters (lid)     =        540         Root MSE        =     0.2051

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
v2pasalie_10 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       ivdem |   .3146488   .1240544     2.54   0.011     .0709594    .5583382
          ld |  -.0007379   .0209457    -0.04   0.972    -.0418831    .0404074
    populism |  -.4759934   .0458662   -10.38   0.000    -.5660918    -.385895
       _cons |   .5166819   .1001231     5.16   0.000     .3200027    .7133611
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         reghdfe v2pasalie_10 ivdem ld persparty populism if s==1,
> a(cowcode year)cluster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 2 HDFE groups                           F(   4,    539) =      28.94
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6289
                                                  Adj R-squared   =     0.5805
                                                  Within R-sq.    =     0.1397
Number of clusters (lid)     =        540         Root MSE        =     0.2052

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
v2pasalie_10 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       ivdem |   .3145444   .1241002     2.53   0.012     .0707651    .5583237
          ld |  -.0008929   .0209178    -0.04   0.966    -.0419832    .0401974
   persparty |  -.0049909   .0545566    -0.09   0.927    -.1121606    .1021788
    populism |  -.4750876   .0463165   -10.26   0.000    -.5660706   -.3841046
       _cons |   .5194643   .1061772     4.89   0.000     .3108924    .7280361
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         
.                         reg workingclass ld persparty if s==1,cluster(lid)

Linear regression                               Number of obs     =      1,182
                                                F(2, 570)         =       8.22
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0477
                                                Root MSE          =     .30467

                                  (Std. err. adjusted for 571 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0468952    .013748     3.41   0.001     .0198923    .0738981
   persparty |  -.1239962   .0639163    -1.94   0.053    -.2495363     .001544
       _cons |   .0314243   .0525821     0.60   0.550    -.0718539    .1347025
------------------------------------------------------------------------------

.                         est store pp1

.                         reg workingclass ld populism if s==1,cluster(lid)

Linear regression                               Number of obs     =      1,122
                                                F(2, 543)         =      13.93
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0783
                                                Root MSE          =     .30667

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0694505   .0152753     4.55   0.000     .0394446    .0994564
    populism |   .2741417   .0764981     3.58   0.000     .1238733    .4244101
       _cons |  -.1898063    .048967    -3.88   0.000    -.2859942   -.0936184
------------------------------------------------------------------------------

.                         est store pp2

.                         reg workingclass ld persparty populism if s==1,cluster(li
> d)

Linear regression                               Number of obs     =      1,122
                                                F(3, 543)         =      10.13
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0901
                                                Root MSE          =     .30483

                                  (Std. err. adjusted for 544 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0534367   .0146126     3.66   0.000     .0247325    .0821409
   persparty |  -.1840837   .0708954    -2.60   0.010    -.3233466   -.0448209
    populism |   .3016695   .0768901     3.92   0.000     .1506311    .4527079
       _cons |  -.0565759   .0599956    -0.94   0.346    -.1744279    .0612761
------------------------------------------------------------------------------

.                         est store pp3

.                         reghdfe workingclass ld persparty if s==1,a(cowcode year)
> cluster(lid)
(dropped 5 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      1,177
Absorbing 2 HDFE groups                           F(   2,    565) =       2.02
Statistics robust to heteroskedasticity           Prob > F        =     0.1340
                                                  R-squared       =     0.3555
                                                  Adj R-squared   =     0.2761
                                                  Within R-sq.    =     0.0077
Number of clusters (lid)     =        566         Root MSE        =     0.2650

                                  (Std. err. adjusted for 566 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0565034   .0287306     1.97   0.050     .0000716    .1129352
   persparty |   .0770648   .0884217     0.87   0.384    -.0966105    .2507402
       _cons |  -.1018588   .1061249    -0.96   0.338    -.3103062    .1065887
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        99           0          99     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         est store pp4

.                         reghdfe workingclass ld populism if s==1,a(cowcode year)c
> luster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 2 HDFE groups                           F(   2,    539) =      16.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4005
                                                  Adj R-squared   =     0.3236
                                                  Within R-sq.    =     0.0753
Number of clusters (lid)     =        540         Root MSE        =     0.2620

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0605996   .0291122     2.08   0.038     .0034123     .117787
    populism |   .4190259   .0749936     5.59   0.000     .2717102    .5663415
       _cons |  -.2133221   .0945778    -2.26   0.025    -.3991084   -.0275358
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         est store pp5

.                         reghdfe workingclass ld persparty populism if s==1,a(cowc
> ode year)cluster(lid)
(dropped 4 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      1,118
Absorbing 2 HDFE groups                           F(   3,    539) =      11.09
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4007
                                                  Adj R-squared   =     0.3231
                                                  Within R-sq.    =     0.0755
Number of clusters (lid)     =        540         Root MSE        =     0.2621

                                  (Std. err. adjusted for 540 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
workingclass | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |    .059663   .0297072     2.01   0.045      .001307    .1180191
   persparty |  -.0294579   .0946584    -0.31   0.756    -.2154025    .1564867
    populism |   .4243729   .0765081     5.55   0.000     .2740823    .5746635
       _cons |  -.1972814   .1129613    -1.75   0.081    -.4191796    .0246169
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        97           0          97     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         est store pp6

.                         label var ld `""Democracy" "age       ""'

.                         label var persparty `""Party      " "{bf:personalism}""'

.                         label var populism `""Party   " "populism""'

.                 coefplot (pp1, msymbol(d))(pp2, msymbol(T))(pp3, msymbol(P)), ord
> er(persparty populism ld)  ///
>                         drop(_cons) xline(0)  mfcolor(white) grid(glcolor(gs15)) 
> ///
>                         levels(95 90) legend(off) xsize(2) ysize(2) xlab(-.25(.25
> ).5)  ///
>                         xtitle("        Coefficient estimate", size(small))saving
> (h1.gph,replace)  ///
>                         ciopts(lwidth(thin)) aspectratio(1.1) scale(.75) title("W
> orking class support: No FE", size(medium) height(2))
(note:  named style P not found in class symbol, default attributes used)
file h1.gph saved

.                 coefplot (pp4, msymbol(d))(pp5, msymbol(T))(pp6, msymbol(P)), ord
> er(persparty populism ld)  ///
>                         drop(_cons) xline(0)  mfcolor(white) grid(glcolor(gs15)) 
> ///
>                         levels(95 90) legend(off) xsize(2) ysize(2) xlab(-.25(.25
> ).5)  ///
>                         xtitle("        Coefficient estimate", size(small))saving
> (h2.gph,replace)  ///
>                         ciopts(lwidth(thin)) aspectratio(1.1) scale(.75) title("W
> orking class support: Two-way FE", size(medium) height(2))
(note:  named style P not found in class symbol, default attributes used)
file h2.gph saved

.                 gr combine h1.gph h2.gph,xsize(8)tit(Populism - but not personali
> sm - associated with working class support)iscale(1.1)
(note:  named style P not found in class symbol, default attributes used)
(note:  named style P not found in class symbol, default attributes used)

.                 gr export "$dir\golden\T-Personalism-Populism-Working-Class.pdf",
> as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -Personalism-Populism-Working-Class.pdf saved as PDF format

. 
. 
.         *************************************************
.         *** Personalist parties and electoral systems ***
.         *************************************************
.                 use pers-use,clear

.                 gen lowerprop = v2elparlel==1 if v2elparlel~=.

.                 gen lowermix = v2elparlel==2 if v2elparlel~=.

.                 gen lowermajor = v2elparlel==0 if v2elparlel~=.

.                 
.                 * Only four countries change pres vs. parl *
.                 egen m_pres = mean(pres) if year==min,by(cowcode)
(1,814 missing values generated)

.                 tab country_name if m_pres>0 & m_pres<1

                    Country name |      Freq.     Percent        Cum.
---------------------------------+-----------------------------------
                         Armenia |          2       10.53       10.53
                         Finland |          9       47.37       57.89
                         Georgia |          5       26.32       84.21
                         Tunisia |          3       15.79      100.00
---------------------------------+-----------------------------------
                           Total |         19      100.00

.                 egen ctag =tag(country) if pres~=.

.                 tab pres if ctag==1 & (m_pres==1 | m_pres==0)

       pres |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         40       43.96       43.96
          1 |         51       56.04      100.00
------------+-----------------------------------
      Total |         91      100.00

.                 drop m_pres     ctag

.                 
.                 * Presidential systems are not just as likely in majoriatarian sy
> stems *
.                 tab  v2elparlel pres, col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

     Lower |
   chamber |
 electoral |         pres
    system |         0          1 |     Total
-----------+----------------------+----------
         0 |       253        311 |       564 
           |     21.24      25.90 |     23.58 
-----------+----------------------+----------
         1 |       694        605 |     1,299 
           |     58.27      50.37 |     54.31 
-----------+----------------------+----------
         2 |       239        280 |       519 
           |     20.07      23.31 |     21.70 
-----------+----------------------+----------
         3 |         5          5 |        10 
           |      0.42       0.42 |      0.42 
-----------+----------------------+----------
     Total |     1,191      1,201 |     2,392 
           |    100.00     100.00 |    100.00 

.                 egen m_v2elparlel=mean(v2elparlel),by(cowcode)

.                 tab m_v2elparlel

m_v2elparle |
          l |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        446       18.65       18.65
    .173913 |         23        0.96       19.61
         .2 |         15        0.63       20.23
         .4 |          5        0.21       20.44
   .5925926 |         27        1.13       21.57
    .862069 |         29        1.21       22.78
   .9310345 |         29        1.21       24.00
          1 |      1,073       44.86       68.85
   1.083333 |         24        1.00       69.86
   1.133333 |         90        3.76       73.62
   1.142857 |         21        0.88       74.50
   1.153846 |         26        1.09       75.59
   1.275862 |         58        2.42       78.01
   1.333333 |         27        1.13       79.14
   1.407407 |         27        1.13       80.27
       1.48 |         25        1.05       81.31
   1.533333 |         30        1.25       82.57
   1.565217 |         23        0.96       83.53
   1.586207 |         29        1.21       84.74
   1.666667 |         30        1.25       85.99
   1.793103 |         29        1.21       87.21
   1.866667 |         15        0.63       87.83
          2 |        256       10.70       98.54
   2.166667 |         30        1.25       99.79
          3 |          5        0.21      100.00
------------+-----------------------------------
      Total |      2,392      100.00

.                 tab country if m_v2elparlel~=0 & m_v2elparlel~=1 & m_v2elparlel~=
> 2

                                country |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                            Afghanistan |          5        0.81        0.81
                                Albania |         29        4.70        5.51
                                Armenia |          5        0.81        6.32
                                Bolivia |         29        4.70       11.02
                               Bulgaria |         30        4.86       15.88
                                Croatia |         29        4.70       20.58
                                Ecuador |         30        4.86       25.45
                               Honduras |         30        4.86       30.31
                                  Italy |         25        4.05       34.36
                                  Japan |         30        4.86       39.22
                                Lebanon |         15        2.43       41.65
                                Lesotho |         27        4.38       46.03
                              Macedonia |         29        4.70       50.73
                             Madagascar |         23        3.73       54.46
                                Moldova |         29        4.70       59.16
                               Mongolia |         27        4.38       63.53
                                  Nepal |         24        3.89       67.42
                            New Zealand |         30        4.86       72.29
                            Philippines |         30        4.86       77.15
                                Romania |         29        4.70       81.85
                           Sierra Leone |         23        3.73       85.58
                               Thailand |         21        3.40       88.98
                                 Turkey |         26        4.21       93.19
                                Ukraine |         27        4.38       97.57
                              Venezuela |         15        2.43      100.00
----------------------------------------+-----------------------------------
                                  Total |        617      100.00

.                 
.                 *** Majoritarian lower house systems becoming less common ***
.                 gen time = year-1990

.                 mlogit v2elparlel time ld i.pregion if v2elparlel~=3,cluster(cowc
> ode)base(0)

Iteration 0:  Log pseudolikelihood = -2391.0129  
Iteration 1:  Log pseudolikelihood = -2037.0542  
Iteration 2:  Log pseudolikelihood = -2012.4797  
Iteration 3:  Log pseudolikelihood =  -2011.591  
Iteration 4:  Log pseudolikelihood =  -2011.588  
Iteration 5:  Log pseudolikelihood =  -2011.588  

Multinomial logistic regression                         Number of obs =  2,382
                                                        Wald chi2(14) =  41.19
                                                        Prob > chi2   = 0.0002
Log pseudolikelihood = -2011.588                        Pseudo R2     = 0.1587

                              (Std. err. adjusted for 105 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
  v2elparlel | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0            |  (base outcome)
-------------+----------------------------------------------------------------
1            |
        time |   .0298383   .0172077     1.73   0.083    -.0038882    .0635649
          ld |  -.1553666   .3483589    -0.45   0.656    -.8381375    .5274044
             |
     pregion |
          2  |   .8588677   1.126411     0.76   0.446    -1.348856    3.066592
          3  |  -.3373081   1.320293    -0.26   0.798    -2.925035    2.250419
          4  |  -3.119818   .8043322    -3.88   0.000    -4.696281   -1.543356
          5  |  -.8706534   .9162388    -0.95   0.342    -2.666449    .9251417
          6  |  -2.909736   .9924463    -2.93   0.003    -4.854896   -.9645774
             |
       _cons |   1.972039   .8943422     2.21   0.027     .2191602    3.724917
-------------+----------------------------------------------------------------
2            |
        time |   .0413564   .0189775     2.18   0.029     .0041612    .0785516
          ld |  -.1100037   .3423732    -0.32   0.748    -.7810428    .5610354
             |
     pregion |
          2  |   .3545309   1.181215     0.30   0.764    -1.960609    2.669671
          3  |  -2.357865   1.606335    -1.47   0.142    -5.506224    .7904944
          4  |  -2.274099   .7741789    -2.94   0.003    -3.791462   -.7567359
          5  |  -1.779035   1.083028    -1.64   0.100     -3.90173    .3436603
          6  |  -.9572994   .7847891    -1.22   0.223    -2.495458    .5808591
             |
       _cons |   .7808705   .9301821     0.84   0.401    -1.042253    2.603994
------------------------------------------------------------------------------

.                 
.                 * Reported models *
.                 reg persparty pres if  year==min,cluster(cowcode)

Linear regression                               Number of obs     =        578
                                                F(1, 103)         =       7.41
                                                Prob > F          =     0.0076
                                                R-squared         =     0.0380
                                                Root MSE          =     .21922

                              (Std. err. adjusted for 104 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        pres |   .0879569   .0323072     2.72   0.008     .0238832    .1520307
       _cons |   .4931907   .0261327    18.87   0.000     .4413626    .5450187
------------------------------------------------------------------------------

.                 est store sys1

.                 reg persparty pres ld if  year==min,cluster(cowcode)

Linear regression                               Number of obs     =        578
                                                F(2, 103)         =      37.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2263
                                                Root MSE          =     .19677

                              (Std. err. adjusted for 104 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        pres |   .0355583   .0268226     1.33   0.188    -.0176381    .0887546
          ld |  -.0833981   .0102275    -8.15   0.000    -.1036819   -.0631142
       _cons |   .7525724   .0361757    20.80   0.000     .6808264    .8243183
------------------------------------------------------------------------------

.                 est store sys2

.                 reg persparty lowerprop lowermix if v2elparlel~=3 & year==min,clu
> ster(cowcode)

Linear regression                               Number of obs     =        573
                                                F(2, 102)         =       1.21
                                                Prob > F          =     0.3033
                                                R-squared         =     0.0106
                                                Root MSE          =     .22201

                              (Std. err. adjusted for 103 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   lowerprop |  -.0199566   .0438819    -0.45   0.650    -.1069961    .0670829
    lowermix |   .0383174    .043733     0.88   0.383    -.0484267    .1250615
       _cons |   .5331599   .0359774    14.82   0.000     .4617989    .6045208
------------------------------------------------------------------------------

.                 est store sys3

.                 reg persparty lowerprop lowermix ld if v2elparlel~=3 & year==min,
> cluster(cowcode)

Linear regression                               Number of obs     =        573
                                                F(3, 102)         =      23.12
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2238
                                                Root MSE          =     .19681

                              (Std. err. adjusted for 103 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   lowerprop |   .0093234   .0317568     0.29   0.770    -.0536661    .0723129
    lowermix |    .043275   .0317124     1.36   0.175    -.0196265    .1061765
          ld |  -.0863471   .0103963    -8.31   0.000    -.1069681   -.0657261
       _cons |    .760675   .0353526    21.52   0.000     .6905534    .8307967
------------------------------------------------------------------------------

.                 est store sys4

.                 reg persparty lowerprop lowermix ld pres if v2elparlel~=3 & year=
> =min,cluster(cowcode)

Linear regression                               Number of obs     =        573
                                                F(4, 102)         =      18.64
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2297
                                                Root MSE          =     .19623

                              (Std. err. adjusted for 103 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   lowerprop |   .0090794   .0314184     0.29   0.773    -.0532387    .0713976
    lowermix |   .0434587   .0317417     1.37   0.174    -.0195008    .1064183
          ld |  -.0826107   .0102247    -8.08   0.000    -.1028913   -.0623302
        pres |   .0358435   .0273683     1.31   0.193    -.0184413    .0901283
       _cons |   .7348997   .0415225    17.70   0.000       .65254    .8172594
------------------------------------------------------------------------------

.                 est store sys5

.                 label var lowermix `""Mixed      " "(Majoritarian)""'

.                 label var lowerprop `""Proportional  " "(Majoritarian)""'

.                 label var ld `""Age of    " "democracy""'

.                 label var pres `""Presidential" "system    ""'

.                 coefplot(sys1, msymbol(O))(sys2, msymbol(T)) , ///
>                         drop(_cons) grid(glcolor(gs15))xline(0,lpattern(dash)) xl
> ab(-.1(.1).2) ///
>                         xtitle(Coefficient estimates) order(remit)level(95 90) ti
> tle("Presidents and prime ministers", ///
>                         size(medium)height(6))xsize(2) ysize(2.5) mlabel format(%
> 9.2g) ///
>                         mlabsize(vsmall)mlabposition(2)mlabgap(*.65) ///
>                         legend(lab(3 "Bivariate")lab(6 "    Adjust for" "democrac
> y age")  ///
>                         order(3 6)size(vsmall)pos(5)col(1)ring(0))saving(h1.gph,r
> eplace)        
file h1.gph saved

.                 coefplot(sys3, msymbol(O))(sys4, msymbol(T)) (sys5, msymbol(d))  
> , ///
>                         drop(_cons) order(lowerprop lowermix pres ld) ///
>                         grid(glcolor(gs15))xline(0,lpattern(dash)) xlab(-.1(.1).2
> ) ///
>                         xtitle(Coefficient estimates)  level(95 90) title("Electo
> ral systems", ///
>                         size(medium)height(6)) xsize(2) ysize(2.5) mlabel format(
> %9.2g) ///
>                         mlabsize(vsmall)mlabposition(2)mlabgap(*.65)legend(lab(3 
> "Bivariate") ///
>                         lab(6 "    Adjust for" "democracy age")lab(9 "  President
> ial" "+ democracy age") ///
>                         size(vsmall)order(3 6 9)pos(5)col(1)ring(0)) ///
>                         saving(h2.gph,replace)
file h2.gph saved

.                 gr combine h1.gph h2.gph

.                 gr export "$dir\golden\Ch2-electoral-system-persparty.pdf",as(pdf
> )replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h2-electoral-system-persparty.pdf saved as PDF format

. 
.                  *** Incentives to cultivate a personalist vote ***
.                  sum pers_rank dom_rank

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   pers_rank |        912    5.531798    3.913314          1         12
    dom_rank |        912    4.732456    3.853338          1         12

.                  qui replace pers_rank=pers_rank/12

.                  qui replace dom_rank=dom_rank/12

.                  reg persparty dom_rank,cluster(cowcode)

Linear regression                               Number of obs     =        912
                                                F(1, 74)          =       0.17
                                                Prob > F          =     0.6815
                                                R-squared         =     0.0016
                                                Root MSE          =     .22705

                               (Std. err. adjusted for 75 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dom_rank |  -.0280707   .0681263    -0.41   0.682    -.1638154    .1076739
       _cons |   .5153536   .0318904    16.16   0.000     .4518107    .5788966
------------------------------------------------------------------------------

.                  est store cult1a

.                  reg persparty dom_rank ld,cluster(cowcode)

Linear regression                               Number of obs     =        912
                                                F(2, 74)          =      28.50
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2960
                                                Root MSE          =     .19077

                               (Std. err. adjusted for 75 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dom_rank |   .0290649   .0510945     0.57   0.571     -.072743    .1308729
          ld |  -.1077318   .0147967    -7.28   0.000    -.1372149   -.0782486
       _cons |    .813887    .045198    18.01   0.000      .723828     .903946
------------------------------------------------------------------------------

.                  est store cult2a

.                  reg persparty dom_rank ld pres,cluster(cowcode)

Linear regression                               Number of obs     =        912
                                                F(3, 74)          =      19.88
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2996
                                                Root MSE          =     .19037

                               (Std. err. adjusted for 75 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dom_rank |   .0289338   .0512003     0.57   0.574    -.0730849    .1309526
          ld |  -.1040393    .014202    -7.33   0.000    -.1323373   -.0757412
        pres |   .0288904   .0348787     0.83   0.410     -.040607    .0983877
       _cons |    .789471   .0445716    17.71   0.000     .7006601    .8782819
------------------------------------------------------------------------------

.                  est store cult3a

.                  reghdfe persparty dom_rank ld pres,a(pregion) cluster(cowcode)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        912
Absorbing 1 HDFE group                            F(   3,     74) =       3.10
Statistics robust to heteroskedasticity           Prob > F        =     0.0318
                                                  R-squared       =     0.3499
                                                  Adj R-squared   =     0.3441
                                                  Within R-sq.    =     0.0465
Number of clusters (cowcode) =         75         Root MSE        =     0.1839

                               (Std. err. adjusted for 75 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    dom_rank |   .0010594   .0633531     0.02   0.987    -.1251744    .1272933
          ld |  -.0442631   .0189123    -2.34   0.022    -.0819467   -.0065795
        pres |   .0566137   .0577452     0.98   0.330    -.0584461    .1716736
       _cons |   .6093975   .0675491     9.02   0.000     .4748031    .7439919
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     pregion |         6           0           6     |
-----------------------------------------------------+

.                  est store cult4a

.                  
.                  reg persparty pers_rank,cluster(cowcode)

Linear regression                               Number of obs     =        912
                                                F(1, 74)          =       0.10
                                                Prob > F          =     0.7491
                                                R-squared         =     0.0010
                                                Root MSE          =     .22711

                               (Std. err. adjusted for 75 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   pers_rank |   .0221913   .0691226     0.32   0.749    -.1155385    .1599211
       _cons |   .4940535   .0371043    13.32   0.000     .4201216    .5679855
------------------------------------------------------------------------------

.                  est store cult1b

.                  reg persparty pers_rank ld,cluster(cowcode)

Linear regression                               Number of obs     =        912
                                                F(2, 74)          =      27.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3005
                                                Root MSE          =     .19015

                               (Std. err. adjusted for 75 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   pers_rank |   .0548216   .0528484     1.04   0.303    -.0504811    .1601244
          ld |  -.1078621   .0144891    -7.44   0.000    -.1367323    -.078992
       _cons |    .800466   .0477886    16.75   0.000     .7052451    .8956869
------------------------------------------------------------------------------

.                  est store cult2b

.                  reg persparty pers_rank ld pres,cluster(cowcode)

Linear regression                               Number of obs     =        912
                                                F(3, 74)          =      19.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3040
                                                Root MSE          =     .18978

                               (Std. err. adjusted for 75 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   pers_rank |   .0540892   .0522463     1.04   0.304    -.0500139    .1581922
          ld |  -.1042329   .0139009    -7.50   0.000     -.131931   -.0765347
        pres |   .0282985   .0346886     0.82   0.417    -.0408201    .0974171
       _cons |   .7768003   .0485364    16.00   0.000     .6800894    .8735112
------------------------------------------------------------------------------

.                  est store cult3b

.                  reghdfe persparty pers_rank ld pres,a(pregion)cluster(cowcode)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        912
Absorbing 1 HDFE group                            F(   3,     74) =       3.11
Statistics robust to heteroskedasticity           Prob > F        =     0.0316
                                                  R-squared       =     0.3509
                                                  Adj R-squared   =     0.3452
                                                  Within R-sq.    =     0.0480
Number of clusters (cowcode) =         75         Root MSE        =     0.1838

                               (Std. err. adjusted for 75 clusters in cowcode)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   pers_rank |   .0264536   .0627794     0.42   0.675    -.0986371    .1515443
          ld |  -.0472758   .0179854    -2.63   0.010    -.0831126   -.0114391
        pres |   .0532173   .0573743     0.93   0.357    -.0611034     .167538
       _cons |   .6081821   .0688592     8.83   0.000     .4709771     .745387
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     pregion |         6           0           6     |
-----------------------------------------------------+

.                  est store cult4b

.                  label var propn "Proportional"

.                  label var pers_rank `""Personal" "vote rank""'

.                 coefplot(cult1b, msymbol(O))(cult2b, msymbol(T))(cult3b, msymbol(
> S)) (cult4b, msymbol(D)), drop(_cons) ///
>                         grid(glcolor(gs15))xline(0,lpattern(dash)) ///
>                         xtitle(Coefficient estimates) order(remit)level(95 90) //
> /
>                         title("Incentives to cultivate a personalist vote" "and s
> election into personalist party rule", ///
>                         size(medium)height(6)) legend(lab(3 "Bivariate") lab(6 "+
>  democracy age") lab(9 "+ Presidential system") lab(12 "+ Region FE") ///
>                         pos(6)ring(1)col(2)) xsize(2) ysize(2.5) mlabel format(%9
> .2g) ///
>                         mlabsize(vsmall)mlabposition(2)mlabgap(*.65)

.                 gr export "$dir\golden\T-Cultivate-persparty.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -Cultivate-persparty.pdf saved as PDF format

.                 
. twoway (scatter   persparty pers_rank) (lpolyci   persparty pers_rank,legend(off)
> xtit(Electoral rules to cultivate a personal vote)ytit("Party personalism ({&thet
> a})"))

. twoway (scatter   persparty dom_rank) (lpolyci   persparty dom_rank,legend(off)xt
> it(Electoral rules to cultivate a personal vote)ytit("Party personalism ({&theta}
> )"))          

. 
. 
.                 ******************************************              
.                 **** Personalist parties and populism ****
.                 ******************************************
.                         use pers-use,clear

.                         egen max=max(year),by(lid)

.                         gen ddem = v2x_poly-ivdem if year==max
(1,800 missing values generated)

.                         egen devdem = max(ddem),by(lid)

.                         gen rho =.
(2,392 missing values generated)

.                         gen n =_n

. 
.                         * Party populism *
.                         krls persparty populism
Iteration =  1, Looloss: 478.387   
Iteration =  2, Looloss: 474.9467  
Iteration =  3, Looloss: 471.6971  
Iteration =  4, Looloss: 468.8694  
Iteration =  5, Looloss: 466.5644  
Iteration =  6, Looloss: 464.7713  
Iteration =  7, Looloss: 463.4058  
Iteration =  8, Looloss: 462.3652  
Iteration =  9, Looloss: 461.5707  

Pointwise Derivatives                                  Number of obs =     2243 
                                                       Lambda        =    8.698 
                                                       Tolerance     =    2.243 
                                                       Sigma         =        1 
                                                       Eff. df       =    6.246 
                                                       R2            =   .06886 
                                                       Looloss       =    460.5

persparty |      Avg.       SE        t    P>|t|        P25       P50       P75    
>    
----------+--------------------------------------------------------------------
 populism |  .278406   .029604    9.404    0.000    .060958   .314976   .478511  
----------+--------------------------------------------------------------------


.                         mat b = e(b)

.                         local b = b[1,1]

.                         replace rho = `b' if n==1
(1 real change made)

.                         krls persparty populism if year==min
Iteration =  1, Looloss: 111.8574  
Iteration =  2, Looloss: 110.9313  
Iteration =  3, Looloss: 110.0902  
Iteration =  4, Looloss: 109.4127  
Iteration =  5, Looloss: 108.9337  
Iteration =  6, Looloss: 108.6431  

Pointwise Derivatives                                  Number of obs =      531 
                                                       Lambda        =    7.972 
                                                       Tolerance     =     .531 
                                                       Sigma         =        1 
                                                       Eff. df       =    4.947 
                                                       R2            =   .07857 
                                                       Looloss       =    108.5

persparty |      Avg.       SE        t    P>|t|        P25       P50       P75    
>    
----------+--------------------------------------------------------------------
 populism |  .193244   .044967    4.297    0.000    .099779   .160365   .208771  
----------+--------------------------------------------------------------------


.                         mat b = e(b)

.                         local b = b[1,1]

.                         replace rho = `b' if n==2
(1 real change made)

.                         krls persparty populism ld ivdem if year==min,d(k)
Iteration =  1, Looloss: 102.5235  
Iteration =  2, Looloss: 98.16115  
Iteration =  3, Looloss: 93.71099  
Iteration =  4, Looloss: 89.73186  
Iteration =  5, Looloss: 86.60201  
Iteration =  6, Looloss: 84.40086  
Iteration =  7, Looloss: 82.99526  
Iteration =  8, Looloss: 82.18905  

Pointwise Derivatives                                  Number of obs =      531 
                                                       Lambda        =    3.899 
                                                       Tolerance     =     .531 
                                                       Sigma         =        3 
                                                       Eff. df       =    17.09 
                                                       R2            =      .34 
                                                       Looloss       =    81.72

persparty |      Avg.       SE        t    P>|t|        P25       P50       P75    
>    
----------+--------------------------------------------------------------------
 populism |  .053108   .034998    1.517    0.130   -.105115   .027424   .203359  
       ld | -.055114   .007138   -7.721    0.000   -.086108  -.050763  -.025766  
    ivdem | -.145057   .060926   -2.381    0.018   -.384056  -.187412   .115026  
----------+--------------------------------------------------------------------


.                         mat b = e(b)

.                         local b = b[1,1]

.                         replace rho = `b' if n==3
(1 real change made)

.                  
.                         twoway bar rho n if n<4,ytit({&rho})barw(.4) xtit("")ylab
> (0 (.1) .3) /// 
>                                 xlab(1  `""All leaders" "All years""'  2 `""All l
> eaders" "First year""'    ///
>                                 3 `""All leaders (531)" "First year" "Adjust for"
>  "consolidation" "', ///
>                                 labsize(vsmall))xscale(range(0.5 3.5))ylab(0 (.05
> ) .25)bcol(gs12) ///
>                                 text(0.26 1  "0.28*",size(small)) text(0.17 2  "0
> .19*",size(small)) ///
>                                 text(0.04 3  "0.05",size(small)) tit(Correlation 
> between party personalism and populism) ///
>                                 note("* {it:p} < 0.05",size(vsmall))

.                         gr export "$dir\golden\Ch2-Persparty-Populist.pdf",as(pdf
> )replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h2-Persparty-Populist.pdf saved as PDF format

.  
.                         * Point-wise marginal effect decreases as democracy chang
> e increases *
.                         twoway lpoly k_populism devdem,legend(off)bw(.1)xtit("Cha
> nge in democracy score during leader's tenure") ///
>                                 ylab(0(.05).25)ytit("{&rho}",size(large))yline(0,
> lcol(red)) ///
>                                 tit("Correlation ({&rho}) between party {it:perso
> nalism{sub:i,t=0}} and {it:populism{sub:i,t=0}}")

.                         gr export "$dir\golden\T-Endogneous-Populism.pdf",as(pdf)
> replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -Endogneous-Populism.pdf saved as PDF format

.                                 
.                         * Correlation in backsliders but not others *
.                         qui sum devdem if populism~=. & year==min

.                         gen bad = devdem<r(mean) -r(sd)  if devdem~=.

.                         tab bad if year==min

        bad |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        530       91.70       91.70
          1 |         48        8.30      100.00
------------+-----------------------------------
      Total |        578      100.00

.                         krls persparty populism ld ivdem if year==min & bad==1
Iteration =  1, Looloss: 8.480136  

Pointwise Derivatives                                  Number of obs =       41 
                                                       Lambda        =    25.99 
                                                       Tolerance     =     .041 
                                                       Sigma         =        3 
                                                       Eff. df       =    1.269 
                                                       R2            =   .06246 
                                                       Looloss       =    8.454

persparty |      Avg.       SE        t    P>|t|        P25       P50       P75    
>    
----------+--------------------------------------------------------------------
 populism |  .031478   .015497    2.031    0.049     .01253   .027931   .053259  
       ld | -.003047   .003206   -0.950    0.348    -.00702  -.001699   -.00014  
    ivdem |  .016625   .024029    0.692    0.493   -.006368   .017729   .044909  
----------+--------------------------------------------------------------------


.                         krls persparty populism ld ivdem if year==min & bad==0
Iteration =  1, Looloss: 93.53867  
Iteration =  2, Looloss: 89.46993  
Iteration =  3, Looloss: 85.34518  
Iteration =  4, Looloss: 81.69531  
Iteration =  5, Looloss: 78.87016  
Iteration =  6, Looloss: 76.92539  
Iteration =  7, Looloss: 75.71642  
Iteration =  8, Looloss: 75.05498  

Pointwise Derivatives                                  Number of obs =      490 
                                                       Lambda        =     5.68 
                                                       Tolerance     =      .49 
                                                       Sigma         =        3 
                                                       Eff. df       =    14.27 
                                                       R2            =    .3364 
                                                       Looloss       =    74.79

persparty |      Avg.       SE        t    P>|t|        P25       P50       P75    
>    
----------+--------------------------------------------------------------------
 populism |  .031269   .034236    0.913    0.362   -.111032    .01559   .160137  
       ld | -.055544   .006762   -8.214    0.000   -.087728  -.050011  -.025283  
    ivdem | -.149513    .05537   -2.700    0.007    -.36712  -.187723   .079967  
----------+--------------------------------------------------------------------


.                                 
.                         * Populism and party personalism do not correspond in Bra
> zil and Colombia *
.                         twoway (line populism year if country=="Brazil",sort)   /
> //
>                                 (line persparty year if country=="Brazil",sort yt
> itle(Level)xtit(Year)legend(lab(1 "Populism") ///
>                                 lab(2  "Party personalism")pos(6)col(3))tit(Brazi
> l))

.                         twoway (lpoly populism year if country=="Colombia",sort) 
> ///
>                                 (lpoly persparty year if country=="Colombia",sort
>  ytitle(Level)xtit(Year)legend(lab(1 "Populism") ///
>                                 lab(2  "Party personalism")pos(6)col(3))tit(Colom
> bia))

.                                 
.                 *************************************************               
.                 **** Selecting into ruling party personalism ****
.                 *************************************************
.                 use pers-use,clear

.                 gen majoritarian=v2elparlel==0 if v2elparlel~=.

.                 gen pubfin = l1v2elpubfin_ord==3 | l1v2elpubfin_ord==4 if l1v2elp
> ubfin_ord~=. & l1v2elpubfin_ord~=2
(409 missing values generated)

.                 gen proportional= v2elparlel==1 | v2elparlel==3

.                 qui gen wealth=e_migdppcln

.                 qui xi:reg e_migdppcln i.cowcode i.year 

.                 qui predict hat

.                 replace wealth=hat if wealth==.
(189 real changes made)

.                 hist wealth
(bin=33, start=6.1592889, width=.15729429)

.                 sum wealth e_migdppcln

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      wealth |      2,392    9.213885    1.132266   6.159289      11.35
 e_migdppcln |      2,203    9.243985    1.115255       6.54      11.35

.                 egen cmin = min(year),by(cowcode)

.                 gen osupdem=l1supdem if year==min
(2,018 missing values generated)

.                 egen isupdem = max(osupdem),by(lid)
(748 missing values generated)

.                 gen devsupdem = supdem-l4supdem
(936 missing values generated)

.                 
.                 local var = "ld l1v2elpubfin l1polar l12gr l1supdem l1v2xps_party
>  wealth"

.                 foreach v of local var {
  2.                         egen z`v' = std(`v')
  3.                 }
(1 missing value generated)
(31 missing values generated)
(117 missing values generated)
(720 missing values generated)
(11 missing values generated)

.                 keep if year==minyr
(1,814 observations deleted)

.                 replace zwealth=zwealth/.5
(578 real changes made)

.                 
.                 * Dynamic correlates of selection into ruling party personalism *
.                 reghdfe persparty zld zl1polar zl1v2elpubfin zwealth zl1v2xps_par
> ty,a(year)cluster(lid)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        565
Absorbing 1 HDFE group                            F(   5,    530) =      40.83
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3110
                                                  Adj R-squared   =     0.2668
                                                  Within R-sq.    =     0.2820
Number of clusters (lid)     =        565         Root MSE        =     0.1906

                                    (Std. err. adjusted for 565 clusters in lid)
--------------------------------------------------------------------------------
               |               Robust
     persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
           zld |  -.0753756   .0113374    -6.65   0.000    -.0976473    -.053104
      zl1polar |   .0276291   .0092013     3.00   0.003     .0095536    .0457045
 zl1v2elpubfin |  -.0320969   .0107814    -2.98   0.003    -.0532765   -.0109174
       zwealth |   .0125158   .0083308     1.50   0.134    -.0038497    .0288813
zl1v2xps_party |  -.0362492   .0134689    -2.69   0.007    -.0627082   -.0097903
         _cons |   .5187719   .0082028    63.24   0.000     .5026578    .5348859
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        30           0          30     |
-----------------------------------------------------+

.                 est store s1a

.                 reghdfe persparty zld zl1polar zl1v2elpubfin zwealth zl1supdem zl
> 1v2xps_party,a(year)cluster(lid)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        366
Absorbing 1 HDFE group                            F(   6,    334) =      24.80
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.3442
                                                  Adj R-squared   =     0.2834
                                                  Within R-sq.    =     0.3053
Number of clusters (lid)     =        366         Root MSE        =     0.1828

                                    (Std. err. adjusted for 366 clusters in lid)
--------------------------------------------------------------------------------
               |               Robust
     persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
           zld |  -.0647953   .0146416    -4.43   0.000    -.0935966    -.035994
      zl1polar |   .0209231   .0109292     1.91   0.056    -.0005755    .0424218
 zl1v2elpubfin |  -.0305225   .0144145    -2.12   0.035    -.0588771   -.0021679
       zwealth |   .0071621   .0121093     0.59   0.555    -.0166579    .0309821
     zl1supdem |   -.022078   .0119957    -1.84   0.067    -.0456747    .0015187
zl1v2xps_party |  -.0413014   .0169032    -2.44   0.015    -.0745516   -.0080512
         _cons |   .5235439   .0101721    51.47   0.000     .5035346    .5435533
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        26           0          26     |
-----------------------------------------------------+

.                 est store s1b

.                 reghdfe persparty zld zl1polar zl1v2elpubfin zwealth zl1v2xps_par
> ty,a(cowcode year)cluster(lid)
(dropped 9 singleton observations)
(MWFE estimator converged in 9 iterations)

HDFE Linear regression                            Number of obs   =        556
Absorbing 2 HDFE groups                           F(   5,    430) =       2.65
Statistics robust to heteroskedasticity           Prob > F        =     0.0224
                                                  R-squared       =     0.5815
                                                  Adj R-squared   =     0.4598
                                                  Within R-sq.    =     0.0284
Number of clusters (lid)     =        556         Root MSE        =     0.1624

                                    (Std. err. adjusted for 556 clusters in lid)
--------------------------------------------------------------------------------
               |               Robust
     persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
           zld |  -.0264454   .0206124    -1.28   0.200     -.066959    .0140683
      zl1polar |   .0178986   .0199381     0.90   0.370    -.0212897    .0570869
 zl1v2elpubfin |  -.0485429   .0285612    -1.70   0.090    -.1046799    .0075941
       zwealth |  -.0549134   .0392804    -1.40   0.163    -.1321189     .022292
zl1v2xps_party |   .0356283   .0482019     0.74   0.460    -.0591124    .1303689
         _cons |   .5248981    .007595    69.11   0.000     .5099701    .5398261
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        92           0          92     |
        year |        30           1          29     |
-----------------------------------------------------+

.                 est store s2a

.                 reghdfe persparty zld zl1polar zl1v2elpubfin zwealth zl1supdem zl
> 1v2xps_party,a(cowcode year)cluster(lid)
(dropped 8 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =        358
Absorbing 2 HDFE groups                           F(   6,    248) =       1.52
Statistics robust to heteroskedasticity           Prob > F        =     0.1715
                                                  R-squared       =     0.6625
                                                  Adj R-squared   =     0.5142
                                                  Within R-sq.    =     0.0270
Number of clusters (lid)     =        358         Root MSE        =     0.1489

                                    (Std. err. adjusted for 358 clusters in lid)
--------------------------------------------------------------------------------
               |               Robust
     persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
           zld |   .0053583   .0330833     0.16   0.871    -.0598018    .0705184
      zl1polar |   .0042522   .0293312     0.14   0.885    -.0535179    .0620222
 zl1v2elpubfin |  -.0655834   .0379743    -1.73   0.085    -.1403766    .0092098
       zwealth |   -.072626   .0497524    -1.46   0.146    -.1706172    .0253652
     zl1supdem |   .0031329   .0309149     0.10   0.919    -.0577564    .0640221
zl1v2xps_party |   .0504852   .0539807     0.94   0.351    -.0558338    .1568042
         _cons |   .5458914   .0215065    25.38   0.000     .5035327    .5882501
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        79           0          79     |
        year |        26           1          25     |
-----------------------------------------------------+

.                 est store s2b

.                 label var zld `""Democracy" "age      ""'

.                 label var zl1polar "Polarization"

.                 label var zl1supdem `""Support for" "democracy""'

.                 label var zl1v2xps_party `""Party " "system" "instit. ""'

.                 label var zwealth `""GDP" "pc  ""'

.                 label var zl1v2elpubfin `""{bf:Publicly}   " "{bf:financed}  " "{
> bf:campaigns}""'

.                 coefplot  (s2a, msymbol(O))(s2b, msymbol(T)), drop(_cons)  grid(g
> lcolor(gs15))xline(0,lpattern(dash))  ///
>                         xtitle(Coefficient estimates) order(zl1v2elpubfin)level(9
> 0) ///
>                         title("Selection into personalist party rule",  ///
>                         size(medium)height(6)) xsize(2) ysize(3) mlabel format(%9
> .2g)  ///
>                         mlabsize(vsmall)mlabposition(2)mlabgap(*.65)xlab(-.15(.05
> ).1) ///
>                         legend(lab(2 "Within, N=556") lab(4 "Within, N=358")pos(6
> )ring(1)col(2))

.                 gr export "$dir\golden\Ch2-selection.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h2-selection.pdf saved as PDF format

.                         
.                         
.                         * support for democracy trends *
.                         krls persparty ivdem ld l1supdem l4supdem 
Iteration =  1, Looloss: 59.62414  
Iteration =  2, Looloss: 57.44227  
Iteration =  3, Looloss: 55.25299  
Iteration =  4, Looloss: 53.326    
Iteration =  5, Looloss: 51.81608  
Iteration =  6, Looloss: 50.72213  
Iteration =  7, Looloss: 49.94919  
Iteration =  8, Looloss: 49.39711  
Iteration =  9, Looloss: 49.01022  
Iteration = 10, Looloss: 48.77468  

Pointwise Derivatives                                  Number of obs =      313 
                                                       Lambda        =    1.495 
                                                       Tolerance     =     .313 
                                                       Sigma         =        4 
                                                       Eff. df       =    19.55 
                                                       R2            =    .3556 
                                                       Looloss       =    48.69

persparty |      Avg.       SE        t    P>|t|        P25       P50       P75    
>    
----------+--------------------------------------------------------------------
    ivdem |  -.18858   .103106   -1.829    0.068   -.406663   -.17468   .046097  
       ld | -.054358   .011911   -4.564    0.000   -.084223  -.058343  -.026975  
 l1supdem |  .042711   .022417    1.905    0.058     .01688   .039603    .06224  
 l4supdem | -.073407   .021657   -3.390    0.001   -.111435  -.069478  -.043289  
----------+--------------------------------------------------------------------


.                         krls persparty ivdem ld devsupdem l4supdem 
Iteration =  1, Looloss: 60.27426  
Iteration =  2, Looloss: 58.08165  
Iteration =  3, Looloss: 55.75934  
Iteration =  4, Looloss: 53.62166  
Iteration =  5, Looloss: 51.91612  
Iteration =  6, Looloss: 50.71879  
Iteration =  7, Looloss: 49.96261  
Iteration =  8, Looloss: 49.53461  

Pointwise Derivatives                                   Number of obs =      313 
                                                        Lambda        =    2.352 
                                                        Tolerance     =     .313 
                                                        Sigma         =        4 
                                                        Eff. df       =    22.19 
                                                        R2            =    .3607 
                                                        Looloss       =    49.34

 persparty |      Avg.       SE        t    P>|t|        P25       P50       P75   
>     
-----------+--------------------------------------------------------------------
     ivdem | -.203319   .082005   -2.479    0.014   -.376852  -.207534  -.015929  
        ld | -.052487   .010732   -4.891    0.000   -.081971  -.050617   -.02419  
 devsupdem |  .059878   .033683    1.778    0.076   -.007391   .052459   .122254  
  l4supdem | -.018619   .012651   -1.472    0.142   -.050513   -.01677    .00825  
-----------+--------------------------------------------------------------------


.                         reg persparty ivdem ld devsupdem l4supdem,cluster(lid)

Linear regression                               Number of obs     =        313
                                                F(4, 312)         =      26.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2699
                                                Root MSE          =     .18282

                                  (Std. err. adjusted for 313 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       ivdem |  -.0700231   .0978117    -0.72   0.475    -.2624771    .1224309
          ld |  -.0675177   .0137095    -4.92   0.000    -.0944925   -.0405429
   devsupdem |   .0599467   .0411402     1.46   0.146    -.0210005     .140894
    l4supdem |  -.0500683   .0139615    -3.59   0.000    -.0775389   -.0225978
       _cons |   .7804254   .0717088    10.88   0.000     .6393313    .9215195
------------------------------------------------------------------------------

.                         reghdfe persparty ivdem ld l1supdem,a(cowcode)cluster(lid
> )
(dropped 7 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        367
Absorbing 1 HDFE group                            F(   3,    283) =       2.14
Statistics robust to heteroskedasticity           Prob > F        =     0.0951
                                                  R-squared       =     0.6196
                                                  Adj R-squared   =     0.5080
                                                  Within R-sq.    =     0.0156
Number of clusters (lid)     =        367         Root MSE        =     0.1494

                                  (Std. err. adjusted for 367 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
   persparty | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       ivdem |   .4205889    .198795     2.12   0.035     .0292843    .8118934
          ld |  -.0306329   .0174711    -1.75   0.081    -.0650227     .003757
    l1supdem |  -.0146517   .0292735    -0.50   0.617    -.0722731    .0429697
       _cons |   .2920857   .1494228     1.95   0.052    -.0020354    .5862068
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        81           0          81     |
-----------------------------------------------------+

.                                 
. 
.         erase h1.gph

.         erase h2.gph

.         erase h3.gph

.         erase h4.gph

.         ************** THE END ******************
. 
.         log close
      name:  <unnamed>
       log:  C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\C
> h2.log
  log type:  text
 closed on:  26 Jul 2023, 15:57:23
-----------------------------------------------------------------------------------
